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Merge pull request #7113

086ee67 Switch to a more efficient rolling Bloom filter (Pieter Wuille)
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laanwj committed Dec 3, 2015
2 parents 8843676 + 086ee67 commit 54a550bef8a8f92b86af318962f8a75bbdef2c4a
Showing with 75 additions and 30 deletions.
  1. +53 −24 src/bloom.cpp
  2. +21 −5 src/bloom.h
  3. +1 −1 src/main.cpp
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@@ -216,30 +216,54 @@ void CBloomFilter::UpdateEmptyFull()
isEmpty = empty;
}
CRollingBloomFilter::CRollingBloomFilter(unsigned int nElements, double fpRate) :
b1(nElements * 2, fpRate, 0), b2(nElements * 2, fpRate, 0)
CRollingBloomFilter::CRollingBloomFilter(unsigned int nElements, double fpRate)
{
// 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;
double logFpRate = log(fpRate);
/* The optimal number of hash functions is log(fpRate) / log(0.5), but
* restrict it to the range 1-50. */
nHashFuncs = std::max(1, std::min((int)round(logFpRate / log(0.5)), 50));
/* In this rolling bloom filter, we'll store between 2 and 3 generations of nElements / 2 entries. */
nEntriesPerGeneration = (nElements + 1) / 2;
uint32_t nMaxElements = nEntriesPerGeneration * 3;
/* The maximum fpRate = pow(1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits), nHashFuncs)
* => pow(fpRate, 1.0 / nHashFuncs) = 1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits)
* => 1.0 - pow(fpRate, 1.0 / nHashFuncs) = exp(-nHashFuncs * nMaxElements / nFilterBits)
* => log(1.0 - pow(fpRate, 1.0 / nHashFuncs)) = -nHashFuncs * nMaxElements / nFilterBits
* => nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - pow(fpRate, 1.0 / nHashFuncs))
* => nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs))
*/
uint32_t nFilterBits = (uint32_t)ceil(-1.0 * nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs)));
data.clear();
/* We store up to 16 'bits' per data element. */
data.resize((nFilterBits + 15) / 16);
reset();
}
/* Similar to CBloomFilter::Hash */
inline unsigned int CRollingBloomFilter::Hash(unsigned int nHashNum, const std::vector<unsigned char>& vDataToHash) const {
return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash) % (data.size() * 16);
}
void CRollingBloomFilter::insert(const std::vector<unsigned char>& vKey)
{
if (nInsertions == 0) {
b1.clear();
} else if (nInsertions == nBloomSize / 2) {
b2.clear();
if (nEntriesThisGeneration == nEntriesPerGeneration) {
nEntriesThisGeneration = 0;
nGeneration++;
if (nGeneration == 4) {
nGeneration = 1;
}
/* Wipe old entries that used this generation number. */
for (uint32_t p = 0; p < data.size() * 16; p++) {
if (get(p) == nGeneration) {
put(p, 0);
}
}
}
b1.insert(vKey);
b2.insert(vKey);
if (++nInsertions == nBloomSize) {
nInsertions = 0;
nEntriesThisGeneration++;
for (int n = 0; n < nHashFuncs; n++) {
uint32_t h = Hash(n, vKey);
put(h, nGeneration);
}
}
@@ -251,10 +275,13 @@ void CRollingBloomFilter::insert(const uint256& hash)
bool CRollingBloomFilter::contains(const std::vector<unsigned char>& vKey) const
{
if (nInsertions < nBloomSize / 2) {
return b2.contains(vKey);
for (int n = 0; n < nHashFuncs; n++) {
uint32_t h = Hash(n, vKey);
if (get(h) == 0) {
return false;
}
}
return b1.contains(vKey);
return true;
}
bool CRollingBloomFilter::contains(const uint256& hash) const
@@ -265,8 +292,10 @@ bool CRollingBloomFilter::contains(const uint256& hash) const
void CRollingBloomFilter::reset()
{
unsigned int nNewTweak = GetRand(std::numeric_limits<unsigned int>::max());
b1.reset(nNewTweak);
b2.reset(nNewTweak);
nInsertions = 0;
nTweak = GetRand(std::numeric_limits<unsigned int>::max());
nEntriesThisGeneration = 0;
nGeneration = 1;
for (std::vector<uint32_t>::iterator it = data.begin(); it != data.end(); it++) {
*it = 0;
}
}
View
@@ -110,8 +110,11 @@ class CBloomFilter
* reset() is provided, which also changes nTweak to decrease the impact of
* false-positives.
*
* contains(item) will always return true if item was one of the last N things
* contains(item) will always return true if item was one of the last N to 1.5*N
* insert()'ed ... but may also return true for items that were not inserted.
*
* It needs around 1.8 bytes per element per factor 0.1 of false positive rate.
* (More accurately: 3/(log(256)*log(2)) * log(1/fpRate) * nElements bytes)
*/
class CRollingBloomFilter
{
@@ -129,10 +132,23 @@ class CRollingBloomFilter
void reset();
private:
unsigned int nBloomSize;
unsigned int nInsertions;
CBloomFilter b1, b2;
};
int nEntriesPerGeneration;
int nEntriesThisGeneration;
int nGeneration;
std::vector<uint32_t> data;
unsigned int nTweak;
int nHashFuncs;
unsigned int Hash(unsigned int nHashNum, const std::vector<unsigned char>& vDataToHash) const;
inline int get(uint32_t position) const {
return (data[(position >> 4) % data.size()] >> (2 * (position & 0xF))) & 0x3;
}
inline void put(uint32_t position, uint32_t val) {
uint32_t& cell = data[(position >> 4) % data.size()];
cell = (cell & ~(((uint32_t)3) << (2 * (position & 0xF)))) | (val << (2 * (position & 0xF)));
}
};
#endif // BITCOIN_BLOOM_H
View
@@ -181,7 +181,7 @@ namespace {
* million to make it highly unlikely for users to have issues with this
* filter.
*
* Memory used: 1.7MB
* Memory used: 1.3 MB
*/
boost::scoped_ptr<CRollingBloomFilter> recentRejects;
uint256 hashRecentRejectsChainTip;

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