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Posted to oak-commits@jackrabbit.apache.org by re...@apache.org on 2025/08/06 10:14:58 UTC

(jackrabbit-oak) 01/01: OAK-11842: Copy Bloom Filter implementation from oak-run-commons to oak-commons for re-use

This is an automated email from the ASF dual-hosted git repository.

reschke pushed a commit to branch OAK-11842
in repository https://gitbox.apache.org/repos/asf/jackrabbit-oak.git

commit e2e16c1a826561dcd37c6b8bc8fee7d942bf4303
Author: Julian Reschke <ju...@gmx.de>
AuthorDate: Wed Aug 6 11:14:36 2025 +0100

    OAK-11842: Copy Bloom Filter implementation from oak-run-commons to oak-commons for re-use
---
 .../jackrabbit/oak/spi/blob/split/BlobIdSet.java   |  16 +-
 oak-commons/pom.xml                                |   4 +
 .../oak/commons/collections/BloomFilter.java       | 188 +++++++++++++++++++
 .../oak/commons/collections/HashUtils.java         |  86 +++++++++
 .../oak/commons/collections/HyperLogLog.java       |  95 ++++++++++
 .../oak/commons/collections/package-info.java      |   2 +-
 .../oak/commons/collections/BloomFilterTest.java   | 200 +++++++++++++++++++++
 .../oak/commons/collections/HyperLogLogTest.java   | 161 +++++++++++++++++
 oak-run-commons/pom.xml                            |   5 +
 .../flatfile/analysis/modules/BinaryId.java        |   8 +-
 .../analysis/modules/DistinctBinarySize.java       |   4 +-
 .../modules/DistinctBinarySizeHistogram.java       |   2 +-
 .../flatfile/analysis/modules/PropertyStats.java   |   4 +-
 .../flatfile/analysis/utils/TopKValues.java        |   3 +-
 .../index/indexer/document/tree/Prefetcher.java    |   6 +-
 .../analysis/modules/PropertyStatsTest.java        |   2 +-
 .../analysis/utils/CountMinSketchTest.java         |   5 +-
 .../flatfile/analysis/utils/TopKValuesTest.java    |  10 +-
 .../document/cache/CacheChangesTracker.java        |  44 ++---
 19 files changed, 789 insertions(+), 56 deletions(-)

diff --git a/oak-blob/src/main/java/org/apache/jackrabbit/oak/spi/blob/split/BlobIdSet.java b/oak-blob/src/main/java/org/apache/jackrabbit/oak/spi/blob/split/BlobIdSet.java
index e62084fcfe..49acb59232 100644
--- a/oak-blob/src/main/java/org/apache/jackrabbit/oak/spi/blob/split/BlobIdSet.java
+++ b/oak-blob/src/main/java/org/apache/jackrabbit/oak/spi/blob/split/BlobIdSet.java
@@ -25,7 +25,6 @@ import java.io.FileNotFoundException;
 import java.io.FileReader;
 import java.io.FileWriter;
 import java.io.IOException;
-import java.nio.charset.StandardCharsets;
 
 import org.apache.commons.io.IOUtils;
 import org.slf4j.Logger;
@@ -33,8 +32,7 @@ import org.slf4j.LoggerFactory;
 
 import org.apache.jackrabbit.guava.common.cache.Cache;
 import org.apache.jackrabbit.guava.common.cache.CacheBuilder;
-import org.apache.jackrabbit.guava.common.hash.BloomFilter;
-import org.apache.jackrabbit.guava.common.hash.Funnels;
+import org.apache.jackrabbit.oak.commons.collections.BloomFilter;
 
 class BlobIdSet {
 
@@ -42,19 +40,19 @@ class BlobIdSet {
 
     private final File store;
 
-    private final BloomFilter<CharSequence> bloomFilter;
+    private final BloomFilter bloomFilter;
 
     private final Cache<String, Boolean> cache;
 
     BlobIdSet(String repositoryDir, String filename) {
         store = new File(new File(repositoryDir), filename);
-        bloomFilter = BloomFilter.create(Funnels.stringFunnel(StandardCharsets.UTF_8), 9000000); // about 8MB
+        bloomFilter = BloomFilter.construct(9000000, 0.03); // 9M entries, 3% false positive rate
         cache = CacheBuilder.newBuilder().maximumSize(1000).build();
         fillBloomFilter();
     }
 
     synchronized boolean contains(String blobId) throws IOException {
-        if (!bloomFilter.apply(blobId)) {
+        if (!bloomFilter.mayContain(blobId)) {
             return false;
         }
         Boolean cached = cache.getIfPresent(blobId);
@@ -64,7 +62,7 @@ class BlobIdSet {
 
         if (isPresentInStore(blobId)) {
             cache.put(blobId, Boolean.TRUE);
-            bloomFilter.put(blobId);
+            bloomFilter.add(blobId);
             return true;
         } else {
             cache.put(blobId, Boolean.FALSE);
@@ -74,7 +72,7 @@ class BlobIdSet {
 
     synchronized void add(String blobId) throws IOException {
         addToStore(blobId);
-        bloomFilter.put(blobId);
+        bloomFilter.add(blobId);
         cache.put(blobId, Boolean.TRUE);
     }
 
@@ -114,7 +112,7 @@ class BlobIdSet {
             reader = new BufferedReader(new FileReader(store));
             String line;
             while ((line = reader.readLine()) != null) {
-                bloomFilter.put(line);
+                bloomFilter.add(line);
             }
         } catch (IOException e) {
             log.error("Can't fill bloom filter", e);
diff --git a/oak-commons/pom.xml b/oak-commons/pom.xml
index 5e8396cd65..8b774a03dc 100644
--- a/oak-commons/pom.xml
+++ b/oak-commons/pom.xml
@@ -102,6 +102,10 @@
       <groupId>org.apache.commons</groupId>
       <artifactId>commons-collections4</artifactId>
     </dependency>
+    <dependency>
+      <groupId>commons-codec</groupId>
+      <artifactId>commons-codec</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.jackrabbit</groupId>
       <artifactId>jackrabbit-jcr-commons</artifactId>
diff --git a/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/BloomFilter.java b/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/BloomFilter.java
new file mode 100644
index 0000000000..3c266ea1bb
--- /dev/null
+++ b/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/BloomFilter.java
@@ -0,0 +1,188 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.jackrabbit.oak.commons.collections;
+
+/**
+ * A Bloom filter implementation.
+ */
+public class BloomFilter {
+
+    private final int k;
+    private final int arraySize;
+    private final long[] data;
+
+    private BloomFilter(long[] data, int k) {
+        this.data = data;
+        this.k = k;
+        this.arraySize = data.length;
+    }
+
+    /**
+     * Construct a Bloom filter. With a fpp of 0.01, the memory usage is roughly 1
+     * byte per entry.
+     *
+     * @param bytes the size in number of bytes (eg. 64_000_000 for 64 MB memory
+     *              usage)
+     * @param fpp   the false-positive probability (eg. 0.01 for a 1% false-positive
+     *              probability)
+     * @return the Bloom filter
+     */
+    public static BloomFilter construct(long n, double fpp) {
+        if (n <= 0) {
+            throw new IllegalArgumentException("n must be greater than 0");
+        }
+        if (fpp <= 0 || fpp >= 1) {
+            throw new IllegalArgumentException("fpp must be between 0 and 1");
+        }
+        long m = calculateBits(n, fpp);
+        int k = calculateK((double) m / n);
+        return new BloomFilter(new long[(int) ((m + 63) / 64)], k);
+    }
+
+    // See also https://hur.st/bloomfilter
+
+    /**
+     * Calculate the best k parameter for a Bloom filter.
+     *
+     * @param bitsPerKey the number of bits per key (eg. 10)
+     * @return the k parameter
+     */
+    public static int calculateK(double bitsPerKey) {
+        return Math.max(1, (int) Math.round(bitsPerKey * Math.log(2)));
+    }
+
+    /**
+     * Calculate the number of bits needed for a Bloom filter, given a number of entries and the k parameter.
+     *
+     * @param n the number of entries (eg. 1_000_000)
+     * @param fpp the false positive probability (eg. 0.01)
+     * @return the bits needed
+     */
+    public static long calculateBits(long n, double fpp) {
+        return (long) Math.ceil((n * Math.log(fpp)) / Math.log(1 / Math.pow(2, Math.log(2))));
+    }
+
+    /**
+     * Calculate the maximum number of entries in the set, given the the memory size
+     * in bits, and a target false positive probability.
+     *
+     * @param bits the number of bits (eg. 10_000_000)
+     * @param fpp  the false positive probability (eg. 0.01)
+     * @return the maximum number of entries to be added
+     */
+    public static long calculateN(long bits, double fpp) {
+        return (long) Math.ceil((bits * Math.log(Math.pow(0.5, Math.log(2))) / Math.log(fpp)));
+    }
+
+    /**
+     * Calculate the false positive probability.
+     *
+     * @param bits the number of bits (eg. 10_000_000)
+     * @param fpp  the false positive probability (eg. 0.01)
+     * @return the maximum number of entries to be added
+     */
+    public static double calculateFpp(long n, long bits, int k) {
+        // p = pow(1 - exp(-k / (m / n)), k)
+        return Math.pow(1 - Math.exp(-k / ((double) bits / n)), k);
+    }
+
+    /**
+     * Add an entry, using an int hash code.
+     *
+     * @param value the value to check
+     */
+    public void add(String value) {
+        add(HashUtils.hash64(value));
+    }
+
+    /**
+     * Add an entry.
+     *
+     * @param hash the hash value (need to be a high quality hash code, with all
+     *             bits having high entropy)
+     */
+    public void add(long hash) {
+        long a = (hash >>> 32) | (hash << 32);
+        long b = hash;
+        for (int i = 0; i < k; i++) {
+            data[HashUtils.reduce((int) (a >>> 32), arraySize)] |= 1L << a;
+            a += b;
+        }
+    }
+
+    /**
+     * Whether the entry may be in the set.
+     *
+     * @param value the value to check
+     * @return true if the entry was added, or, with a certain false positive
+     *         probability, even if it was not added
+     */
+    public boolean mayContain(String value) {
+        return mayContain(HashUtils.hash64(value));
+    }
+
+    /**
+     * Whether the entry may be in the set.
+     *
+     * @param hash the hash value (need to be a high quality hash code, with all
+     *             bits having high entropy)
+     * @return true if the entry was added, or, with a certain false positive
+     *         probability, even if it was not added
+     */
+    public boolean mayContain(long hash) {
+        long a = (hash >>> 32) | (hash << 32);
+        long b = hash;
+        for (int i = 0; i < k; i++) {
+            if ((data[HashUtils.reduce((int) (a >>> 32), arraySize)] & 1L << a) == 0) {
+                return false;
+            }
+            a += b;
+        }
+        return true;
+    }
+
+    /**
+     * Get the number of bits needed for the array.
+     *
+     * @return the number of bits
+     */
+    public long getBitCount() {
+        return data.length * 64L;
+    }
+
+    public int getK() {
+        return k;
+    }
+
+    /**
+     * Get the estimated entry count (number of distinct items added). This
+     * operation is relatively slow, as it loops over all the entries.
+     *
+     * @return the estimated entry count, or Long.MAX_VALUE if the number can not be estimated.
+     */
+    public long getEstimatedEntryCount() {
+        long x = 0;
+        for (long d : data) {
+            x += Long.bitCount(d);
+        }
+        double m = getBitCount();
+        return (long) (-(m / k) * Math.log(1 - (x / m)));
+    }
+
+} 
\ No newline at end of file
diff --git a/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/HashUtils.java b/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/HashUtils.java
new file mode 100644
index 0000000000..90d998664c
--- /dev/null
+++ b/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/HashUtils.java
@@ -0,0 +1,86 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.jackrabbit.oak.commons.collections;
+
+import java.nio.charset.StandardCharsets;
+import org.apache.commons.codec.digest.MurmurHash3;
+
+/**
+ * A hash function utility class.
+ */
+public class HashUtils {
+
+    private HashUtils() {
+        // utility class
+    }
+
+    /**
+     * Calculate a 64-bit hash value from a string.
+     *
+     * @param s the string
+     * @return the hash value
+     */
+    public static long hash64(String s) {
+        return MurmurHash3.hash128(s.getBytes(StandardCharsets.UTF_8))[0];
+    }
+
+    /**
+     * Calculate a 64-bit hash value from a value, using a seed.
+     *
+     * The current algorithm used the finalizer of the MurmurHash3 hash function,
+     * but callers shouldn't rely on that.
+     *
+     * @param x    the value
+     * @param seed the seed
+     * @return the hash value
+     */
+    public static long hash64(long x, long seed) {
+        x += seed;
+        x = (x ^ (x >>> 33)) * 0xff51afd7ed558ccdL;
+        x = (x ^ (x >>> 33)) * 0xc4ceb9fe1a85ec53L;
+        x = x ^ (x >>> 33);
+        return x;
+    }
+
+    /**
+     * Calculate a 64-bit hash value from a value. The input is a 64-bit value and
+     * the output is a 64-bit values. Two different inputs are never mapped to the
+     * same output. The operation is reversible.
+     *
+     * @param x the value
+     * @return the hash value
+     */
+    public static long hash64(long x) {
+        return hash64(x, 100);
+    }
+
+    /**
+     * Shrink the hash to a value 0..n. Kind of like modulo, but using
+     * multiplication and shift, which are faster to compute.
+     *
+     * @param hash the hash
+     * @param n the maximum of the result
+     * @return the reduced value
+     */
+    public static int reduce(int hash, int n) {
+        // http://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
+        return (int) (((hash & 0xffffffffL) * (n & 0xffffffffL)) >>> 32);
+    }
+
+} 
\ No newline at end of file
diff --git a/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/HyperLogLog.java b/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/HyperLogLog.java
new file mode 100644
index 0000000000..2054decff0
--- /dev/null
+++ b/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/HyperLogLog.java
@@ -0,0 +1,95 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.jackrabbit.oak.commons.collections;
+
+import java.util.HashSet;
+
+/**
+ * A HyperLogLog implementation.
+ */
+public class HyperLogLog {
+
+    private final double amm2;
+    private final int m;
+    private final byte[] counters;
+    private final int maxSmallSetSize;
+    private HashSet<Long> smallSet;
+
+    public HyperLogLog(int m, int maxSmallSetSize) {
+        this.maxSmallSetSize = maxSmallSetSize;
+        if (maxSmallSetSize > 0) {
+            smallSet = new HashSet<>();
+        } else {
+            smallSet = null;
+        }
+        if (m < 16) {
+            throw new IllegalArgumentException("Must be >= 16, is " + m);
+        }
+        if (Integer.bitCount(m) != 1) {
+            throw new IllegalArgumentException("Must be a power of 2, is " + m);
+        }
+        this.m = m;
+        double am;
+        switch (m) {
+        case 32:
+            am = 0.697;
+            break;
+        case 64:
+            am = 0.709;
+            break;
+        default:
+            am = 0.7213 / (1.0 + 1.079 / m);
+        }
+        amm2 = am * m * m;
+        this.counters = new byte[m];
+    }
+
+    public void add(long hash) {
+        if (smallSet != null) {
+            smallSet.add(hash);
+            if (smallSet.size() > maxSmallSetSize) {
+                smallSet = null;
+            }
+        }
+        int i = (int) (hash & (m - 1));
+        counters[i] = (byte) Math.max(counters[i], 1 + Long.numberOfLeadingZeros(hash));
+    }
+
+    public long estimate() {
+        if (smallSet != null) {
+            return smallSet.size();
+        }
+        double sum = 0;
+        int countZero = 0;
+        for(int c : counters) {
+            countZero += c == 0 ? 1 : 0;
+            sum += 1. / (1L << (c & 0xff));
+        }
+        if (sum == 0) {
+            sum = 1;
+        }
+        long est = (long) (1. / sum * amm2);
+        if (est <= 5 * m && countZero > 0) {
+            // linear counting
+            est = (long) (m * Math.log((double) m / countZero));
+        }
+        return est;
+    }
+
+} 
\ No newline at end of file
diff --git a/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/package-info.java b/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/package-info.java
index 81c20db5ca..5d1490f210 100644
--- a/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/package-info.java
+++ b/oak-commons/src/main/java/org/apache/jackrabbit/oak/commons/collections/package-info.java
@@ -21,7 +21,7 @@
  * Utilities for Java collections and streams.
  */
 @Internal(since = "1.0.0")
-@Version("2.2.0")
+@Version("2.3.0")
 package org.apache.jackrabbit.oak.commons.collections;
 import org.apache.jackrabbit.oak.commons.annotations.Internal;
 import org.osgi.annotation.versioning.Version;
diff --git a/oak-commons/src/test/java/org/apache/jackrabbit/oak/commons/collections/BloomFilterTest.java b/oak-commons/src/test/java/org/apache/jackrabbit/oak/commons/collections/BloomFilterTest.java
new file mode 100644
index 0000000000..464de8c4ef
--- /dev/null
+++ b/oak-commons/src/test/java/org/apache/jackrabbit/oak/commons/collections/BloomFilterTest.java
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.jackrabbit.oak.commons.collections;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertThrows;
+
+import org.junit.Test;
+
+/**
+ * Tests the Bloom filter implementation.
+ */
+public class BloomFilterTest {
+
+    @Test
+    public void calculateBits() {
+        assertEquals(9_585_059, BloomFilter.calculateBits(1_000_000, 0.01));
+        // test some extreme values
+        assertEquals(0, BloomFilter.calculateBits(1, 1.0));
+        assertEquals(1, BloomFilter.calculateBits(1, 0.99));
+        assertEquals(0, BloomFilter.calculateBits(0, 0.0));
+        assertTrue(BloomFilter.calculateBits(Integer.MAX_VALUE, 0.0) > Integer.MAX_VALUE);
+    }
+
+    @Test
+    public void calculateK() {
+        assertEquals(7, BloomFilter.calculateK(10));
+        // test some extreme values
+        assertEquals(1, BloomFilter.calculateK(1));
+        assertEquals(1, BloomFilter.calculateK(0));
+        assertEquals(69, BloomFilter.calculateK(100));
+    }
+
+    @Test
+    public void calculateN() {
+        assertEquals(11, BloomFilter.calculateN(100, 0.01));
+        // test some extreme values
+        assertEquals(1, BloomFilter.calculateN(1, 0.01));
+        assertEquals(1, BloomFilter.calculateN(1, 0.1));
+        assertEquals(0, BloomFilter.calculateN(0, 0.01));
+    }
+
+    @Test
+    public void construct() {
+        BloomFilter f = BloomFilter.construct(100, 0.01);
+        assertEquals(960, f.getBitCount());
+        assertEquals(7, f.getK());
+    }
+
+    @Test
+    public void fpp() {
+        for (double fpp = 0.001; fpp < 1; fpp *= 2) {
+            int size = 500_000;
+            BloomFilter f = BloomFilter.construct(size, fpp);
+            for (int i = 0; i < size; i++) {
+                f.add(HashUtils.hash64(i));
+            }
+            for (int i = 0; i < size; i++) {
+                assertTrue(f.mayContain(HashUtils.hash64(i)));
+            }
+            int falsePositives = 0;
+            for (int i = 0; i < size; i++) {
+                if (f.mayContain(HashUtils.hash64(i + size))) {
+                    falsePositives++;
+                }
+            }
+            double realFpp = (double) falsePositives / size;
+            // expected to be within 10%
+            assertTrue("expected fpp: " + fpp + " got: " + realFpp, realFpp >= fpp * 0.9 && realFpp <= fpp * 1.1);
+            long est = f.getEstimatedEntryCount();
+            assertTrue("expected n: " + size + " got: " + est, size >= est * 0.9 && size <= est * 1.1);
+
+            double fpp2 = BloomFilter.calculateFpp(size, f.getBitCount(), f.getK());
+            assertTrue("expected fpp: " + fpp + " got: " + fpp2, fpp2 >= fpp * 0.9 && fpp2 <= fpp * 1.1);
+        }
+    }
+
+    @Test
+    public void estimatedEntryCount() {
+        // let's assume we have a 1 KB Bloom filter with a false positive rate of 1%:
+        double fpp = 0.01;
+        long bits = 1000 * 8;
+        long n = BloomFilter.calculateN(bits, fpp);
+        BloomFilter bloom = BloomFilter.construct(n, fpp);
+        // and a HyperLogLog of 1 KB:
+        HyperLogLog hll = new HyperLogLog(1024, 0);
+        // now we calculate estimations with both the Bloom filter and HyperLogLog
+        for(int i = 0; i < 20_000; i++) {
+            long x = HashUtils.hash64(i);
+            bloom.add(x);
+            hll.add(x);
+            if (i > 0 && i % 1000 == 0) {
+                long estBloom = bloom.getEstimatedEntryCount();
+                long estHll = hll.estimate();
+                int errBloom = (int) (Math.abs((double) i / estBloom - 1) * 10000);
+                int errHll = (int) (Math.abs((double) i / estHll - 1) * 10000);
+                if (i < 10_000) {
+                    assertTrue(errBloom < 1000);
+                } else {
+                    assertEquals(Long.MAX_VALUE, estBloom);
+                }
+                assertTrue(errHll < 1000);
+            }
+        }
+    }
+
+    @Test
+    public void filterFunctionality() {
+        BloomFilter filter = BloomFilter.construct(100, 0.01);
+        String testValue = "test-value";
+
+        // Initially should not contain anything
+        assertFalse(filter.mayContain(testValue));
+
+        // Add the item and verify it's found
+        filter.add(testValue);
+        assertTrue(filter.mayContain(testValue));
+
+        // Verify another value is not found
+        assertFalse(filter.mayContain("different-value"));
+    }
+
+    @Test
+    public void filterWithMultipleEntries() {
+        BloomFilter filter = BloomFilter.construct(100, 0.01);
+
+        // Add multiple entries
+        for (int i = 0; i < 100; i++) {
+            filter.add("value-" + i);
+        }
+
+        // Verify all entries are found
+        for (int i = 0; i < 100; i++) {
+            assertTrue(filter.mayContain("value-" + i));
+        }
+    }
+
+    @Test
+    public void falsePositiveProbability() {
+        // Create a filter with high false positive probability for testing
+        double fpp = 0.3;
+        BloomFilter filter = BloomFilter.construct(100, fpp);
+
+        // Fill the filter to capacity
+        for (int i = 0; i < 100; i++) {
+            filter.add("existing-" + i);
+        }
+
+        // Test with values not in the filter
+        int falsePositives = 0;
+        int trials = 1000;
+
+        for (int i = 0; i < trials; i++) {
+            if (filter.mayContain("nonexistent-" + i)) {
+                falsePositives++;
+            }
+        }
+
+        // The false positive rate should be approximately fpp
+        double actualFpp = (double) falsePositives / trials;
+        assertTrue("False positive rate should be close to expected, got " + actualFpp + " expected " + fpp, Math.abs(actualFpp - fpp) < 0.15);
+    }
+
+    @Test
+    public void invalidEntries() {
+        // Should throw exception for entries < 1
+        assertThrows(IllegalArgumentException.class,() -> BloomFilter.construct(0, 0.01));
+    }
+
+    @Test
+    public void invalidFppZero() {
+        // Should throw exception for fpp <= 0
+        assertThrows(IllegalArgumentException.class,() -> BloomFilter.construct(100, 0.0));
+    }
+
+    @Test
+    public void invalidFppOne() {
+        // Should throw exception for fpp >= 1
+        assertThrows(IllegalArgumentException.class,() -> BloomFilter.construct(100, 1.0));
+    }    
+
+} 
\ No newline at end of file
diff --git a/oak-commons/src/test/java/org/apache/jackrabbit/oak/commons/collections/HyperLogLogTest.java b/oak-commons/src/test/java/org/apache/jackrabbit/oak/commons/collections/HyperLogLogTest.java
new file mode 100644
index 0000000000..fd3fb39bff
--- /dev/null
+++ b/oak-commons/src/test/java/org/apache/jackrabbit/oak/commons/collections/HyperLogLogTest.java
@@ -0,0 +1,161 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.jackrabbit.oak.commons.collections;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotEquals;
+import static org.junit.Assert.assertTrue;
+
+import org.junit.Test;
+
+public class HyperLogLogTest {
+
+    @Test(expected = IllegalArgumentException.class)
+    public void illegalHyperLogLogTooSmall() {
+        new HyperLogLog(8, 0);
+    }
+
+    @Test(expected = IllegalArgumentException.class)
+    public void illegalHyperLogLogNotPowerOfTwo() {
+        new HyperLogLog(30, 0);
+    }
+
+    @Test
+    public void smallSet() {
+        HyperLogLog hll100 = new HyperLogLog(16, 100);
+        assertEquals(0, hll100.estimate());
+        HyperLogLog hll0 = new HyperLogLog(16, 0);
+        assertEquals(0, hll0.estimate());
+        for (int i = 0; i < 10_000; i++) {
+            hll100.add(i % 100);
+            hll0.add(i % 100);
+        }
+        assertEquals(100, hll100.estimate());
+        assertNotEquals(100, hll0.estimate());
+    }
+
+    @Test
+    public void test() {
+        int testCount = 50;
+        for (int m = 16; m <= 128; m *= 2) {
+            double avg = Math.sqrt(averageOverRange(m, 30_000, testCount, false, 2));
+            int min, max;
+            switch (m) {
+            case 16:
+                min = 22;
+                max = 23;
+                break;
+            case 32:
+                min = 15;
+                max = 16;
+                break;
+            case 64:
+                min = 10;
+                max = 11;
+                break;
+            case 128:
+                min = 7;
+                max = 8;
+                break;
+            default:
+                min = 0;
+                max = 0;
+                break;
+            }
+            assertTrue("m " + m + " expected " + min + ".." + max + " got " + avg, min < avg && avg < max);
+        }
+    }
+
+    private static double averageOverRange(int m, long maxSize, int testCount, boolean debug, double exponent) {
+        double sum = 0;
+        int count = 0;
+        for (long size = 1; size <= 20; size++) {
+            sum += test(m, size, testCount, debug, exponent);
+            count++;
+        }
+        for (long size = 22; size <= 300; size += size / 5) {
+            sum += test(m, size, testCount, debug, exponent);
+            count++;
+        }
+        for (long size = 400; size <= maxSize; size *= 2) {
+            sum += test(m, size, testCount, debug, exponent);
+            count++;
+        }
+        return sum / count;
+    }
+
+    private static double test(int m, long size, int testCount, boolean debug, double exponent) {
+        long x = 0;
+        long min = Long.MAX_VALUE, max = Long.MIN_VALUE;
+        long ns = System.nanoTime();
+        double sumSquareError = 0;
+        double sum = 0;
+        double sumFirst = 0;
+        int repeat = 10;
+        int runs = 2;
+        for (int test = 0; test < testCount; test++) {
+            HyperLogLog hll;
+            hll = new HyperLogLog(m, 0);
+            long baseX = x;
+            for (int i = 0; i < size; i++) {
+                hll.add(HashUtils.hash64(x));
+                x++;
+            }
+            long e = hll.estimate();
+            sum += e;
+            min = Math.min(min, e);
+            max = Math.max(max, e);
+            long error = e - size;
+            sumSquareError += error * error;
+            sumFirst += e;
+            for (int add = 0; add < repeat; add++) {
+                long x2 = baseX;
+                for (int i = 0; i < size; i++) {
+                    hll.add(HashUtils.hash64(x2));
+                    x2++;
+                }
+            }
+            e = hll.estimate();
+            sum += e;
+            min = Math.min(min, e);
+            max = Math.max(max, e);
+            error = e - size;
+            sumSquareError += error * error;
+        }
+        ns = System.nanoTime() - ns;
+        long nsPerItem = ns / testCount / runs / (1 + repeat) / size;
+        double stdDev = Math.sqrt(sumSquareError / testCount / runs);
+        double relStdDevP = stdDev / size * 100;
+        int biasFirstP = (int) (100 * (sumFirst / testCount / size) - 100);
+        int biasP = (int) (100 * (sum / testCount / runs / size) - 100);
+        if (debug) {
+            System.out.println("m " + m + " size " + size + " relStdDev% " + (int) relStdDevP +
+                    " range " + min + ".." + max +
+                    " biasFirst% " + biasFirstP +
+                    " bias% " + biasP +
+                    " avg " + (sum / testCount / runs) +
+                    " time " + nsPerItem);
+        }
+        // we try to reduce the relStdDevP, make sure there are no large values
+        // (trying to reduce sumSquareError directly
+        // would mean we care more about larger sets, but we don't)
+        return Math.pow(relStdDevP, exponent);
+    }
+
+} 
\ No newline at end of file
diff --git a/oak-run-commons/pom.xml b/oak-run-commons/pom.xml
index e1288dccc2..efd3657e51 100644
--- a/oak-run-commons/pom.xml
+++ b/oak-run-commons/pom.xml
@@ -110,6 +110,11 @@
             <artifactId>oak-jcr</artifactId>
             <version>${project.version}</version>
         </dependency>
+        <dependency>
+            <groupId>org.apache.jackrabbit</groupId>
+            <artifactId>oak-commons</artifactId>
+            <version>${project.version}</version>
+        </dependency>
 
         <dependency>
             <groupId>org.jetbrains</groupId>
diff --git a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/BinaryId.java b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/BinaryId.java
index 3ec93f175d..741c1cceae 100644
--- a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/BinaryId.java
+++ b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/BinaryId.java
@@ -20,7 +20,7 @@ package org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.modul
 
 import java.util.Objects;
 
-import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.Hash;
+import org.apache.jackrabbit.oak.commons.collections.HashUtils;
 
 /**
  * A binary id.
@@ -51,9 +51,9 @@ public class BinaryId {
         // we need to hash again because some of the bits are fixed
         // in case of UUIDs: always a "4" here: xxxxxxxx-xxxx-4xxx
         // (the hash64 is a reversible mapping, so there is no risk of conflicts)
-        this.v0 = Hash.hash64(Long.parseUnsignedLong(buff.substring(0, 16), 16));
-        this.v1 = Hash.hash64(Long.parseUnsignedLong(buff.substring(16, 32), 16));
-        this.v2 = Hash.hash64(Long.parseUnsignedLong(buff.substring(32, Math.min(48, buff.length())), 16));
+        this.v0 = HashUtils.hash64(Long.parseUnsignedLong(buff.substring(0, 16), 16));
+        this.v1 = HashUtils.hash64(Long.parseUnsignedLong(buff.substring(16, 32), 16));
+        this.v2 = HashUtils.hash64(Long.parseUnsignedLong(buff.substring(32, Math.min(48, buff.length())), 16));
     }
 
     @Override
diff --git a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/DistinctBinarySize.java b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/DistinctBinarySize.java
index 272c7f357b..878375f1f8 100644
--- a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/DistinctBinarySize.java
+++ b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/DistinctBinarySize.java
@@ -28,8 +28,8 @@ import java.util.stream.Collectors;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.stream.NodeData;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.stream.NodeProperty;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.stream.NodeProperty.ValueType;
-import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.BloomFilter;
-import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.HyperLogLog;
+import org.apache.jackrabbit.oak.commons.collections.BloomFilter;
+import org.apache.jackrabbit.oak.commons.collections.HyperLogLog;
 
 /**
  * Collects the number and size of distinct binaries.
diff --git a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/DistinctBinarySizeHistogram.java b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/DistinctBinarySizeHistogram.java
index 349a3fe35b..061aa39d52 100644
--- a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/DistinctBinarySizeHistogram.java
+++ b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/DistinctBinarySizeHistogram.java
@@ -27,7 +27,7 @@ import java.util.stream.Collectors;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.stream.NodeData;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.stream.NodeProperty;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.stream.NodeProperty.ValueType;
-import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.HyperLogLog;
+import org.apache.jackrabbit.oak.commons.collections.HyperLogLog;
 
 /**
  * A histogram of distinct binaries. For each size range, we calculate the
diff --git a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/PropertyStats.java b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/PropertyStats.java
index 80227f5744..c749a1ab59 100644
--- a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/PropertyStats.java
+++ b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/PropertyStats.java
@@ -31,7 +31,7 @@ import java.util.stream.Collectors;
 
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.stream.NodeData;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.stream.NodeProperty;
-import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.Hash;
+import org.apache.jackrabbit.oak.commons.collections.HashUtils;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.HyperLogLog3Linear64;
 import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.TopKValues;
 
@@ -123,7 +123,7 @@ public class PropertyStats implements StatsCollector {
         stats.values += p.getValues().length;
         if (stats.count > MIN_PROPERTY_COUNT) {
             for (String v : p.getValues()) {
-                long hash = Hash.hash64(v.hashCode(), seed);
+                long hash = HashUtils.hash64(v.hashCode(), seed);
                 stats.hll = HyperLogLog3Linear64.add(stats.hll, hash);
                 stats.size += v.length();
                 stats.maxSize = Math.max(stats.maxSize, v.length());
diff --git a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/TopKValues.java b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/TopKValues.java
index 2c235f2197..48b4a63b0b 100644
--- a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/TopKValues.java
+++ b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/TopKValues.java
@@ -21,6 +21,7 @@ package org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils
 import java.util.ArrayList;
 import java.util.HashMap;
 
+import org.apache.jackrabbit.oak.commons.collections.HashUtils;
 import org.apache.jackrabbit.oak.commons.json.JsopBuilder;
 
 /**
@@ -91,7 +92,7 @@ public class TopKValues {
         if (countedCount > 1000) {
             skipRemaining = SKIP;
         }
-        long hash = Hash.hash64(value.hashCode());
+        long hash = HashUtils.hash64(value);
         long est = sketch.addAndEstimate(hash);
         if (est < min) {
             return;
diff --git a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/tree/Prefetcher.java b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/tree/Prefetcher.java
index 10992f5c06..8f4e6d2cac 100644
--- a/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/tree/Prefetcher.java
+++ b/oak-run-commons/src/main/java/org/apache/jackrabbit/oak/index/indexer/document/tree/Prefetcher.java
@@ -34,8 +34,8 @@ import org.apache.jackrabbit.oak.api.Blob;
 import org.apache.jackrabbit.oak.api.PropertyState;
 import org.apache.jackrabbit.oak.api.Type;
 import org.apache.jackrabbit.oak.index.indexer.document.NodeStateEntry;
-import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.Hash;
-import org.apache.jackrabbit.oak.index.indexer.document.flatfile.analysis.utils.HyperLogLog;
+import org.apache.jackrabbit.oak.commons.collections.HashUtils;
+import org.apache.jackrabbit.oak.commons.collections.HyperLogLog;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -240,7 +240,7 @@ public class Prefetcher {
         // and then we use a secondary hash
         // otherwise the estimation is way off
         int h = blob.getContentIdentity().hashCode();
-        return Hash.hash64(h | (blob.length() << 32));
+        return HashUtils.hash64(h | (blob.length() << 32));
     }
 
     static enum PrefetchType {
diff --git a/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/PropertyStatsTest.java b/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/PropertyStatsTest.java
index 7803a54400..5fe819335d 100644
--- a/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/PropertyStatsTest.java
+++ b/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/modules/PropertyStatsTest.java
@@ -67,7 +67,7 @@ public class PropertyStatsTest {
             pc.add(n);
         }
         assertEquals("PropertyStats\n"
-                + "skewed weight 3 count 1000000 distinct 394382 avgSize 7 maxSize 11 top {\"skipped\":899091,\"counted\":90910,\"false\":25583,\"true\":25518,\"-411461567\":1,\"1483286044\":1,\"1310925467\":1,\"-1752252714\":1,\"-1433290908\":1,\"-1209544007\":1}\n"
+                + "skewed weight 3 count 1000000 distinct 394382 avgSize 7 maxSize 11 top {\"skipped\":899091,\"counted\":90910,\"false\":25618,\"true\":25543,\"-411461567\":1,\"1483286044\":1,\"1310925467\":1,\"-1752252714\":1,\"-1433290908\":1,\"-1209544007\":1}\n"
                 + "", pc.toString());
     }
 
diff --git a/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/CountMinSketchTest.java b/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/CountMinSketchTest.java
index 92935ca286..212c0fba22 100644
--- a/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/CountMinSketchTest.java
+++ b/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/CountMinSketchTest.java
@@ -24,6 +24,7 @@ import static org.junit.Assert.assertTrue;
 import java.util.Arrays;
 import java.util.Random;
 
+import org.apache.jackrabbit.oak.commons.collections.HashUtils;
 import org.junit.Test;
 
 public class CountMinSketchTest {
@@ -83,11 +84,11 @@ public class CountMinSketchTest {
                     }
                     CountMinSketch est = new CountMinSketch(5, 16);
                     for (int i = 0; i < size; i++) {
-                        est.add(Hash.hash64(x + data[i]));
+                        est.add(HashUtils.hash64(x + data[i]));
                     }
                     int[] counts = getCounts(data);
                     for (int i = 0; i < 10; i++) {
-                        long e = est.estimate(Hash.hash64(x + i));
+                        long e = est.estimate(HashUtils.hash64(x + i));
                         long expectedPercent = (int) (100. * counts[i] / size);
                         long estPercent = (int) (100. * e / size);
                         if (debug) {
diff --git a/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/TopKValuesTest.java b/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/TopKValuesTest.java
index 696e2e724f..dfd36a6eba 100644
--- a/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/TopKValuesTest.java
+++ b/oak-run-commons/src/test/java/org/apache/jackrabbit/oak/index/indexer/document/flatfile/analysis/utils/TopKValuesTest.java
@@ -30,17 +30,17 @@ public class TopKValuesTest {
     public void test() {
         TopKValues v = new TopKValues(3);
         Random r = new Random(1);
-        for(int i=0; i<1000000; i++) {
-            if(r.nextBoolean()) {
+        for (int i = 0; i < 1000000; i++) {
+            if (r.nextBoolean()) {
                 v.add("common" + r.nextInt(2));
             } else {
                 v.add("rare" + r.nextInt(100));
             }
         }
-        assertEquals("{\"notSkewed\":5,\"skipped\":908191,\"counted\":91809,\"common1\":24849,\"common0\":24652,\"rare13\":2374}", v.toString());
+        assertEquals("{\"notSkewed\":5,\"skipped\":908191,\"counted\":91809,\"common0\":24231,\"common1\":23844,\"rare13\":2722}", v.toString());
         assertEquals(91809, v.getCount());
-        assertEquals(24849, v.getTopCount());
-        assertEquals(24652, v.getSecondCount());
+        assertEquals(24231, v.getTopCount());
+        assertEquals(23844, v.getSecondCount());
     }
 
 }
diff --git a/oak-store-document/src/main/java/org/apache/jackrabbit/oak/plugins/document/cache/CacheChangesTracker.java b/oak-store-document/src/main/java/org/apache/jackrabbit/oak/plugins/document/cache/CacheChangesTracker.java
index 14e30cd673..196f6188d4 100644
--- a/oak-store-document/src/main/java/org/apache/jackrabbit/oak/plugins/document/cache/CacheChangesTracker.java
+++ b/oak-store-document/src/main/java/org/apache/jackrabbit/oak/plugins/document/cache/CacheChangesTracker.java
@@ -16,14 +16,13 @@
  */
 package org.apache.jackrabbit.oak.plugins.document.cache;
 
-import org.apache.jackrabbit.guava.common.hash.BloomFilter;
-import org.apache.jackrabbit.guava.common.hash.Funnel;
-import org.apache.jackrabbit.guava.common.hash.PrimitiveSink;
+import org.apache.jackrabbit.oak.commons.collections.BloomFilter;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import java.io.Closeable;
 import java.util.List;
+import java.util.concurrent.atomic.AtomicReference;
 import java.util.function.Predicate;
 
 public class CacheChangesTracker implements Closeable {
@@ -62,10 +61,10 @@ public class CacheChangesTracker implements Closeable {
         changeTrackers.remove(this);
 
         if (LOG.isDebugEnabled()) {
-            if (lazyBloomFilter.filter == null) {
+            if (lazyBloomFilter.filterRef.get() == null) {
                 LOG.debug("Disposing CacheChangesTracker for {}, no filter was needed", keyFilter);
             } else {
-                LOG.debug("Disposing CacheChangesTracker for {}, filter fpp was: {}", keyFilter, lazyBloomFilter.filter.expectedFpp());
+                LOG.debug("Disposing CacheChangesTracker for {}, filter fpp was: {}", keyFilter, LazyBloomFilter.FPP);
             }
         }
     }
@@ -76,38 +75,33 @@ public class CacheChangesTracker implements Closeable {
 
         private final int entries;
 
-        private volatile BloomFilter<String> filter;
+        private final AtomicReference<BloomFilter> filterRef;
 
         public LazyBloomFilter(int entries) {
             this.entries = entries;
+            this.filterRef = new AtomicReference<>();
         }
 
         public synchronized void put(String entry) {
-            getFilter().put(entry);
+            getFilter().add(entry);
         }
 
         public boolean mightContain(String entry) {
-            if (filter == null) {
-                return false;
-            } else {
-                synchronized (this) {
-                    return filter.mightContain(entry);
-                }
-            }
+            BloomFilter f = filterRef.get();
+            return f != null && f.mayContain(entry);
         }
 
-        private BloomFilter<String> getFilter() {
-            if (filter == null) {
-                filter = BloomFilter.create(new Funnel<String>() {
-                    private static final long serialVersionUID = -7114267990225941161L;
-
-                    @Override
-                    public void funnel(String from, PrimitiveSink into) {
-                        into.putUnencodedChars(from);
-                    }
-                }, entries, FPP);
+        private BloomFilter getFilter() {
+            BloomFilter result = filterRef.get();
+            if (result == null) {
+                BloomFilter newFilter = BloomFilter.construct(entries, FPP);
+                if (filterRef.compareAndSet(null, newFilter)) {
+                    result = newFilter;
+                } else {
+                    result = filterRef.get();
+                }
             }
-            return filter;
+            return result;
         }
     }
 }