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>

src/bloom.cpp

@@ -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:
+     * 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
+     * - nElements * log(fp rate) / ln(2)^2
- * We ignore filter parameters which will create a bloom filter larger than the protocol limits
+     * 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),
+    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
+     * 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
+     * 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
+     * See https://en.wikipedia.org/wiki/Bloom_filter for an explanation of these formulas
- */
+     */
-isFull(false),
+    isFull(false),
-isEmpty(false),
+    isEmpty(false),
-nHashFuncs(min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)),
+    nHashFuncs(min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)),
-nTweak(nTweakIn),
+    nTweak(nTweakIn),
-nFlags(nFlagsIn)
+    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;
+}

src/bloom.h

@@ -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

src/test/bloom_tests.cpp

@@ -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()