bloom.cpp raw
1 // Copyright (c) 2012-2022 The Limenka 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
16 #include <algorithm>
17 #include <cmath>
18 #include <cstdlib>
19 #include <limits>
20 #include <vector>
21
22 static constexpr double LN2SQUARED = 0.4804530139182014246671025263266649717305529515945455;
23 static constexpr double LN2 = 0.6931471805599453094172321214581765680755001343602552;
24
25 CBloomFilter::CBloomFilter(const unsigned int nElements, const double nFPRate, const unsigned int nTweakIn, unsigned char nFlagsIn) :
26 /**
27 * The ideal size for a bloom filter with a given number of elements and false positive rate is:
28 * - nElements * log(fp rate) / ln(2)^2
29 * We ignore filter parameters which will create a bloom filter larger than the protocol limits
30 */
31 vData(std::min((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)), MAX_BLOOM_FILTER_SIZE * 8) / 8),
32 /**
33 * The ideal number of hash functions is filter size * ln(2) / number of elements
34 * Again, we ignore filter parameters which will create a bloom filter with more hash functions than the protocol limits
35 * See https://en.wikipedia.org/wiki/Bloom_filter for an explanation of these formulas
36 */
37 nHashFuncs(std::min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)),
38 nTweak(nTweakIn),
39 nFlags(nFlagsIn)
40 {
41 }
42
43 inline unsigned int CBloomFilter::Hash(unsigned int nHashNum, Span<const unsigned char> vDataToHash) const
44 {
45 // 0xFBA4C795 chosen as it guarantees a reasonable bit difference between nHashNum values.
46 return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash) % (vData.size() * 8);
47 }
48
49 void CBloomFilter::insert(Span<const unsigned char> vKey)
50 {
51 if (vData.empty()) // Avoid divide-by-zero (CVE-2013-5700)
52 return;
53 for (unsigned int i = 0; i < nHashFuncs; i++)
54 {
55 unsigned int nIndex = Hash(i, vKey);
56 // Sets bit nIndex of vData
57 vData[nIndex >> 3] |= (1 << (7 & nIndex));
58 }
59 }
60
61 void CBloomFilter::insert(const COutPoint& outpoint)
62 {
63 DataStream stream{};
64 stream << outpoint;
65 insert(MakeUCharSpan(stream));
66 }
67
68 bool CBloomFilter::contains(Span<const unsigned char> vKey) const
69 {
70 if (vData.empty()) // Avoid divide-by-zero (CVE-2013-5700)
71 return true;
72 for (unsigned int i = 0; i < nHashFuncs; i++)
73 {
74 unsigned int nIndex = Hash(i, vKey);
75 // Checks bit nIndex of vData
76 if (!(vData[nIndex >> 3] & (1 << (7 & nIndex))))
77 return false;
78 }
79 return true;
80 }
81
82 bool CBloomFilter::contains(const COutPoint& outpoint) const
83 {
84 DataStream stream{};
85 stream << outpoint;
86 return contains(MakeUCharSpan(stream));
87 }
88
89 bool CBloomFilter::IsWithinSizeConstraints() const
90 {
91 return vData.size() <= MAX_BLOOM_FILTER_SIZE && nHashFuncs <= MAX_HASH_FUNCS;
92 }
93
94 bool CBloomFilter::IsRelevantAndUpdate(const CTransaction& tx)
95 {
96 bool fFound = false;
97 // Match if the filter contains the hash of tx
98 // for finding tx when they appear in a block
99 if (vData.empty()) // zero-size = "match-all" filter
100 return true;
101 const Txid& hash = tx.GetHash();
102 if (contains(hash.ToUint256()))
103 fFound = true;
104
105 for (unsigned int i = 0; i < tx.vout.size(); i++)
106 {
107 const CTxOut& txout = tx.vout[i];
108 // Match if the filter contains any arbitrary script data element in any scriptPubKey in tx
109 // If this matches, also add the specific output that was matched.
110 // This means clients don't have to update the filter themselves when a new relevant tx
111 // is discovered in order to find spending transactions, which avoids round-tripping and race conditions.
112 CScript::const_iterator pc = txout.scriptPubKey.begin();
113 std::vector<unsigned char> data;
114 while (pc < txout.scriptPubKey.end())
115 {
116 opcodetype opcode;
117 if (!txout.scriptPubKey.GetOp(pc, opcode, data))
118 break;
119 if (data.size() != 0 && contains(data))
120 {
121 fFound = true;
122 if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_ALL)
123 insert(COutPoint(hash, i));
124 else if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_P2PUBKEY_ONLY)
125 {
126 std::vector<std::vector<unsigned char> > vSolutions;
127 TxoutType type = Solver(txout.scriptPubKey, vSolutions);
128 if (type == TxoutType::PUBKEY || type == TxoutType::MULTISIG) {
129 insert(COutPoint(hash, i));
130 }
131 }
132 break;
133 }
134 }
135 }
136
137 if (fFound)
138 return true;
139
140 for (const CTxIn& txin : tx.vin)
141 {
142 // Match if the filter contains an outpoint tx spends
143 if (contains(txin.prevout))
144 return true;
145
146 // Match if the filter contains any arbitrary script data element in any scriptSig in tx
147 CScript::const_iterator pc = txin.scriptSig.begin();
148 std::vector<unsigned char> data;
149 while (pc < txin.scriptSig.end())
150 {
151 opcodetype opcode;
152 if (!txin.scriptSig.GetOp(pc, opcode, data))
153 break;
154 if (data.size() != 0 && contains(data))
155 return true;
156 }
157 }
158
159 return false;
160 }
161
162 CRollingBloomFilter::CRollingBloomFilter(const unsigned int nElements, const double fpRate)
163 {
164 double logFpRate = log(fpRate);
165 /* The optimal number of hash functions is log(fpRate) / log(0.5), but
166 * restrict it to the range 1-50. */
167 nHashFuncs = std::max(1, std::min((int)round(logFpRate / log(0.5)), 50));
168 /* In this rolling bloom filter, we'll store between 2 and 3 generations of nElements / 2 entries. */
169 nEntriesPerGeneration = (nElements + 1) / 2;
170 uint32_t nMaxElements = nEntriesPerGeneration * 3;
171 /* The maximum fpRate = pow(1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits), nHashFuncs)
172 * => pow(fpRate, 1.0 / nHashFuncs) = 1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits)
173 * => 1.0 - pow(fpRate, 1.0 / nHashFuncs) = exp(-nHashFuncs * nMaxElements / nFilterBits)
174 * => log(1.0 - pow(fpRate, 1.0 / nHashFuncs)) = -nHashFuncs * nMaxElements / nFilterBits
175 * => nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - pow(fpRate, 1.0 / nHashFuncs))
176 * => nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs))
177 */
178 uint32_t nFilterBits = (uint32_t)ceil(-1.0 * nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs)));
179 data.clear();
180 /* For each data element we need to store 2 bits. If both bits are 0, the
181 * bit is treated as unset. If the bits are (01), (10), or (11), the bit is
182 * treated as set in generation 1, 2, or 3 respectively.
183 * These bits are stored in separate integers: position P corresponds to bit
184 * (P & 63) of the integers data[(P >> 6) * 2] and data[(P >> 6) * 2 + 1]. */
185 data.resize(((nFilterBits + 63) / 64) << 1);
186 reset();
187 }
188
189 /* Similar to CBloomFilter::Hash */
190 static inline uint32_t RollingBloomHash(unsigned int nHashNum, uint32_t nTweak, Span<const unsigned char> vDataToHash)
191 {
192 return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash);
193 }
194
195 void CRollingBloomFilter::insert(Span<const unsigned char> vKey)
196 {
197 if (nEntriesThisGeneration == nEntriesPerGeneration) {
198 nEntriesThisGeneration = 0;
199 nGeneration++;
200 if (nGeneration == 4) {
201 nGeneration = 1;
202 }
203 uint64_t nGenerationMask1 = 0 - (uint64_t)(nGeneration & 1);
204 uint64_t nGenerationMask2 = 0 - (uint64_t)(nGeneration >> 1);
205 /* Wipe old entries that used this generation number. */
206 for (uint32_t p = 0; p < data.size(); p += 2) {
207 uint64_t p1 = data[p], p2 = data[p + 1];
208 uint64_t mask = (p1 ^ nGenerationMask1) | (p2 ^ nGenerationMask2);
209 data[p] = p1 & mask;
210 data[p + 1] = p2 & mask;
211 }
212 }
213 nEntriesThisGeneration++;
214
215 for (int n = 0; n < nHashFuncs; n++) {
216 uint32_t h = RollingBloomHash(n, nTweak, vKey);
217 int bit = h & 0x3F;
218 /* 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. */
219 uint32_t pos = FastRange32(h, data.size());
220 /* The lowest bit of pos is ignored, and set to zero for the first bit, and to one for the second. */
221 data[pos & ~1U] = (data[pos & ~1U] & ~(uint64_t{1} << bit)) | (uint64_t(nGeneration & 1)) << bit;
222 data[pos | 1] = (data[pos | 1] & ~(uint64_t{1} << bit)) | (uint64_t(nGeneration >> 1)) << bit;
223 }
224 }
225
226 bool CRollingBloomFilter::contains(Span<const unsigned char> vKey) const
227 {
228 for (int n = 0; n < nHashFuncs; n++) {
229 uint32_t h = RollingBloomHash(n, nTweak, vKey);
230 int bit = h & 0x3F;
231 uint32_t pos = FastRange32(h, data.size());
232 /* If the relevant bit is not set in either data[pos & ~1] or data[pos | 1], the filter does not contain vKey */
233 if (!(((data[pos & ~1U] | data[pos | 1]) >> bit) & 1)) {
234 return false;
235 }
236 }
237 return true;
238 }
239
240 void CRollingBloomFilter::reset()
241 {
242 nTweak = FastRandomContext().rand<unsigned int>();
243 nEntriesThisGeneration = 0;
244 nGeneration = 1;
245 std::fill(data.begin(), data.end(), 0);
246 }
247