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Rolling bloom filter class

For when you need to keep track of the last N items
you've seen, and can tolerate some false-positives.

Rebased-by: Pieter Wuille <pieter.wuille@gmail.com>
tags/v0.15.1
Gavin Andresen 6 years ago
parent
commit
69a5f8be0a
3 changed files with 173 additions and 16 deletions
  1. 67
    16
      src/bloom.cpp
  2. 28
    0
      src/bloom.h
  3. 78
    0
      src/test/bloom_tests.cpp

+ 67
- 16
src/bloom.cpp View File

@@ -21,22 +21,33 @@
using namespace std;

CBloomFilter::CBloomFilter(unsigned int nElements, double nFPRate, unsigned int nTweakIn, unsigned char nFlagsIn) :
/**
* The ideal size for a bloom filter with a given number of elements and false positive rate is:
* - nElements * log(fp rate) / ln(2)^2
* We ignore filter parameters which will create a bloom filter larger than the protocol limits
*/
vData(min((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)), MAX_BLOOM_FILTER_SIZE * 8) / 8),
/**
* The ideal number of hash functions is filter size * ln(2) / number of elements
* Again, we ignore filter parameters which will create a bloom filter with more hash functions than the protocol limits
* See https://en.wikipedia.org/wiki/Bloom_filter for an explanation of these formulas
*/
isFull(false),
isEmpty(false),
nHashFuncs(min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)),
nTweak(nTweakIn),
nFlags(nFlagsIn)
/**
* The ideal size for a bloom filter with a given number of elements and false positive rate is:
* - nElements * log(fp rate) / ln(2)^2
* We ignore filter parameters which will create a bloom filter larger than the protocol limits
*/
vData(min((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)), MAX_BLOOM_FILTER_SIZE * 8) / 8),
/**
* The ideal number of hash functions is filter size * ln(2) / number of elements
* Again, we ignore filter parameters which will create a bloom filter with more hash functions than the protocol limits
* See https://en.wikipedia.org/wiki/Bloom_filter for an explanation of these formulas
*/
isFull(false),
isEmpty(false),
nHashFuncs(min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)),
nTweak(nTweakIn),
nFlags(nFlagsIn)
{
}

// Private constructor used by CRollingBloomFilter
CBloomFilter::CBloomFilter(unsigned int nElements, double nFPRate, unsigned int nTweakIn) :
vData((unsigned int)(-1 / LN2SQUARED * nElements * log(nFPRate)) / 8),
isFull(false),
isEmpty(true),
nHashFuncs((unsigned int)(vData.size() * 8 / nElements * LN2)),
nTweak(nTweakIn),
nFlags(BLOOM_UPDATE_NONE)
{
}

@@ -197,3 +208,43 @@ void CBloomFilter::UpdateEmptyFull()
isFull = full;
isEmpty = empty;
}

CRollingBloomFilter::CRollingBloomFilter(unsigned int nElements, double fpRate, unsigned int nTweak) :
b1(nElements * 2, fpRate, nTweak), b2(nElements * 2, fpRate, nTweak)
{
// 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.
nBloomSize = nElements * 2;
nInsertions = 0;
}

void CRollingBloomFilter::insert(const std::vector<unsigned char>& vKey)
{
if (nInsertions == 0) {
b1.clear();
} else if (nInsertions == nBloomSize / 2) {
b2.clear();
}
b1.insert(vKey);
b2.insert(vKey);
if (++nInsertions == nBloomSize) {
nInsertions = 0;
}
}

bool CRollingBloomFilter::contains(const std::vector<unsigned char>& vKey) const
{
if (nInsertions < nBloomSize / 2) {
return b2.contains(vKey);
}
return b1.contains(vKey);
}

void CRollingBloomFilter::clear()
{
b1.clear();
b2.clear();
nInsertions = 0;
}

+ 28
- 0
src/bloom.h View File

@@ -53,6 +53,10 @@ private:

unsigned int Hash(unsigned int nHashNum, const std::vector<unsigned char>& vDataToHash) const;

// Private constructor for CRollingBloomFilter, no restrictions on size
CBloomFilter(unsigned int nElements, double nFPRate, unsigned int nTweak);
friend class CRollingBloomFilter;

public:
/**
* Creates a new bloom filter which will provide the given fp rate when filled with the given number of elements
@@ -97,4 +101,28 @@ public:
void UpdateEmptyFull();
};

/**
* RollingBloomFilter is a probabilistic "keep track of most recently inserted" set.
* Construct it with the number of items to keep track of, and a false-positive rate.
*
* contains(item) will always return true if item was one of the last N things
* insert()'ed ... but may also return true for items that were not inserted.
*/
class CRollingBloomFilter
{
public:
CRollingBloomFilter(unsigned int nElements, double nFPRate, unsigned int nTweak);

void insert(const std::vector<unsigned char>& vKey);
bool contains(const std::vector<unsigned char>& vKey) const;

void clear();

private:
unsigned int nBloomSize;
unsigned int nInsertions;
CBloomFilter b1, b2;
};


#endif // BITCOIN_BLOOM_H

+ 78
- 0
src/test/bloom_tests.cpp View File

@@ -8,6 +8,7 @@
#include "clientversion.h"
#include "key.h"
#include "merkleblock.h"
#include "random.h"
#include "serialize.h"
#include "streams.h"
#include "uint256.h"
@@ -459,4 +460,81 @@ BOOST_AUTO_TEST_CASE(merkle_block_4_test_update_none)
BOOST_CHECK(!filter.contains(COutPoint(uint256S("0x02981fa052f0481dbc5868f4fc2166035a10f27a03cfd2de67326471df5bc041"), 0)));
}

static std::vector<unsigned char> RandomData()
{
uint256 r = GetRandHash();
return std::vector<unsigned char>(r.begin(), r.end());
}

BOOST_AUTO_TEST_CASE(rolling_bloom)
{
// last-100-entry, 1% false positive:
CRollingBloomFilter rb1(100, 0.01, 0);

// Overfill:
static const int DATASIZE=399;
std::vector<unsigned char> data[DATASIZE];
for (int i = 0; i < DATASIZE; i++) {
data[i] = RandomData();
rb1.insert(data[i]);
}
// Last 100 guaranteed to be remembered:
for (int i = 299; i < DATASIZE; i++) {
BOOST_CHECK(rb1.contains(data[i]));
}

// false positive rate is 1%, so we should get about 100 hits if
// testing 10,000 random keys. We get worst-case false positive
// behavior when the filter is as full as possible, which is
// when we've inserted one minus an integer multiple of nElement*2.
unsigned int nHits = 0;
for (int i = 0; i < 10000; i++) {
if (rb1.contains(RandomData()))
++nHits;
}
// Run test_bitcoin with --log_level=message to see BOOST_TEST_MESSAGEs:
BOOST_TEST_MESSAGE("RollingBloomFilter got " << nHits << " false positives (~100 expected)");

// Insanely unlikely to get a fp count outside this range:
BOOST_CHECK(nHits > 25);
BOOST_CHECK(nHits < 175);

BOOST_CHECK(rb1.contains(data[DATASIZE-1]));
rb1.clear();
BOOST_CHECK(!rb1.contains(data[DATASIZE-1]));

// Now roll through data, make sure last 100 entries
// are always remembered:
for (int i = 0; i < DATASIZE; i++) {
if (i >= 100)
BOOST_CHECK(rb1.contains(data[i-100]));
rb1.insert(data[i]);
}

// Insert 999 more random entries:
for (int i = 0; i < 999; i++) {
rb1.insert(RandomData());
}
// Sanity check to make sure the filter isn't just filling up:
nHits = 0;
for (int i = 0; i < DATASIZE; i++) {
if (rb1.contains(data[i]))
++nHits;
}
// Expect about 5 false positives, more than 100 means
// something is definitely broken.
BOOST_TEST_MESSAGE("RollingBloomFilter got " << nHits << " false positives (~5 expected)");
BOOST_CHECK(nHits < 100);

// last-1000-entry, 0.01% false positive:
CRollingBloomFilter rb2(1000, 0.001, 0);
for (int i = 0; i < DATASIZE; i++) {
rb2.insert(data[i]);
}
// ... room for all of them:
for (int i = 0; i < DATASIZE; i++) {
BOOST_CHECK(rb2.contains(data[i]));
}
}

BOOST_AUTO_TEST_SUITE_END()

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