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Posted to issues@commons.apache.org by GitBox <gi...@apache.org> on 2020/03/07 17:16:02 UTC
[GitHub] [commons-collections] Claudenw commented on a change in pull
request #137: WIP: CountingBloomFilter
Claudenw commented on a change in pull request #137: WIP: CountingBloomFilter
URL: https://github.com/apache/commons-collections/pull/137#discussion_r389283827
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File path: src/main/java/org/apache/commons/collections4/bloomfilter/ArrayCountingBloomFilter.java
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@@ -0,0 +1,396 @@
+/*
+ * 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.commons.collections4.bloomfilter;
+
+import java.util.BitSet;
+import java.util.HashSet;
+import java.util.NoSuchElementException;
+import java.util.PrimitiveIterator;
+import java.util.PrimitiveIterator.OfInt;
+import java.util.function.Consumer;
+import java.util.function.IntConsumer;
+import java.util.Set;
+
+import org.apache.commons.collections4.bloomfilter.hasher.Hasher;
+import org.apache.commons.collections4.bloomfilter.hasher.Shape;
+import org.apache.commons.collections4.bloomfilter.hasher.StaticHasher;
+
+/**
+ * A counting Bloom filter using an array to track counts for each enabled bit
+ * index.
+ *
+ * <p>Any operation that results in negative counts or integer overflow of counts will
+ * mark this filter as invalid. This transition is not reversible. The counts for the
+ * filter immediately prior to the operation that create invalid counts can be recovered.
+ * See the documentation in {@link #isValid()} for details.
+ *
+ * <p>All the operations in the filter assume the counts are currently valid. Behaviour
+ * of an invalid filter is undefined. It will no longer function identically to a standard
+ * Bloom filter that is the merge of all the Bloom filters that have been added
+ * to and not later subtracted from the counting Bloom filter.
+ *
+ * <p>The maximum supported number of items that can be stored in the filter is
+ * limited by the maximum array size combined with the {@link Shape}. For
+ * example an implementation using a {@link Shape} with a false-positive
+ * probability of 1e-6 and {@link Integer#MAX_VALUE} bits can reversibly store
+ * approximately 75 million items using 20 hash functions per item with a memory
+ * consumption of approximately 8 GB.
+ *
+ * @since 4.5
+ * @see Shape
+ */
+public class ArrayCountingBloomFilter extends AbstractBloomFilter implements CountingBloomFilter {
+
+ /**
+ * The count of each bit index in the filter.
+ */
+ private final int[] counts;
+
+ /**
+ * The state flag. This is a bitwise OR of the entire history of all updated
+ * counts. If negative then a negative count or integer overflow has occurred on
+ * one or more counts in the history of the filter and the state is invalid.
+ *
+ * <p>Maintenance of this state flag is branch-free for improved performance. It
+ * eliminates a conditional check for a negative count during remove/subtract
+ * operations and a conditional check for integer overflow during merge/add
+ * operations.
+ *
+ * <p>Note: Integer overflow is unlikely in realistic usage scenarios. A count
+ * that overflows indicates that the number of items in the filter exceeds the
+ * maximum possible size (number of bits) of any Bloom filter constrained by
+ * integer indices. At this point the filter is most likely full (all bits are
+ * non-zero) and thus useless.
+ *
+ * <p>Negative counts are a concern if the filter is used incorrectly by
+ * removing an item that was never added. It is expected that a user of a
+ * counting Bloom filter will not perform this action as it is a mistake.
+ * Enabling an explicit recovery path for negative or overflow counts is a major
+ * performance burden not deemed necessary for the unlikely scenarios when an
+ * invalid state is created. Maintenance of the state flag is a concession to
+ * flag improper use that should not have a major performance impact.
+ */
+ private int state;
+
+ /**
+ * An iterator of all indexes with non-zero counts.
+ *
+ * <p>In the event that the filter state is invalid any index with a negative count
+ * will also be produced by the iterator.
+ */
+ private class IndexIterator implements PrimitiveIterator.OfInt {
+ /** The next non-zero index (or counts.length). */
+ private int next;
+
+ /**
+ * Create an instance.
+ */
+ IndexIterator() {
+ advance();
+ }
+
+ /**
+ * Advance to the next non-zero index.
+ */
+ void advance() {
+ while (next < counts.length && counts[next] == 0) {
+ next++;
+ }
+ }
+
+ @Override
+ public boolean hasNext() {
+ return next < counts.length;
+ }
+
+ @Override
+ public int nextInt() {
+ if (hasNext()) {
+ final int result = next++;
+ advance();
+ return result;
+ }
+ // Currently unreachable as the iterator is only used by
+ // the StaticHasher which iterates correctly.
+ throw new NoSuchElementException();
+ }
+ }
+
+ /**
+ * Constructs an empty counting Bloom filter with the specified shape.
+ *
+ * @param shape the shape of the filter
+ */
+ public ArrayCountingBloomFilter(final Shape shape) {
+ super(shape);
+ counts = new int[shape.getNumberOfBits()];
+ }
+
+ /**
+ * Constructs a counting Bloom filter from a hasher and a shape.
+ *
+ * <p>The filter will be equal to the result of merging the hasher with an empty
+ * filter; specifically duplicate indexes in the hasher are ignored.
+ *
+ * @param hasher the hasher to build the filter from
+ * @param shape the shape of the filter
+ * @throws IllegalArgumentException if the hasher cannot generate indices for
+ * the shape
+ * @see #merge(Hasher)
+ */
+ public ArrayCountingBloomFilter(final Hasher hasher, final Shape shape) {
+ super(shape);
+ // Given the filter is empty we can optimise the operation of merge(hasher)
+ verifyHasher(hasher);
+ // Delay array allocation until after hasher is verified
+ counts = new int[shape.getNumberOfBits()];
+ // All counts are zero. Ignore duplicates by initialising to 1
+ hasher.getBits(shape).forEachRemaining((IntConsumer) idx -> counts[idx] = 1);
+ }
+
+ @Override
+ public int cardinality() {
+ int size = 0;
+ for (final int c : counts) {
+ if (c != 0) {
+ size++;
+ }
+ }
+ return size;
+ }
+
+ @Override
+ public boolean contains(BloomFilter other) {
+ // The AbstractBloomFilter implementation converts both filters to long[] bits.
+ // This would involve checking all indexes in this filter against zero.
+ // Ideally we use an iterator of bit indexes to allow fail-fast on the
+ // first bit index that is zero.
+ if (other instanceof ArrayCountingBloomFilter) {
+ verifyShape(other);
+ return contains(((ArrayCountingBloomFilter) other).iterator());
+ }
+
+ // Note:
+ // This currently creates a StaticHasher which stores all the indexes.
+ // It would greatly benefit from direct generation of the index iterator
+ // avoiding the intermediate storage.
+ return contains(other.getHasher());
+ }
+
+ @Override
+ public boolean contains(final Hasher hasher) {
+ verifyHasher(hasher);
+ return contains(hasher.getBits(getShape()));
+ }
+
+ /**
+ * Return true if this filter is has non-zero counts for each index in the iterator.
+ *
+ * @param iter the iterator
+ * @return true if this filter contains all the indexes
+ */
+ private boolean contains(final OfInt iter) {
+ while (iter.hasNext()) {
+ if (counts[iter.nextInt()] == 0) {
+ return false;
+ }
+ }
+ return true;
+ }
+
+ @Override
+ public long[] getBits() {
+ final BitSet bs = new BitSet();
+ for (int i = 0; i < counts.length; i++) {
+ if (counts[i] != 0) {
+ bs.set(i);
+ }
+ }
+ return bs.toLongArray();
+ }
+
+ @Override
+ public StaticHasher getHasher() {
+ return new StaticHasher(iterator(), getShape());
+ }
+
+ /**
+ * Returns an iterator over the enabled indexes in this filter.
+ * Any index with a non-zero count is considered enabled.
+ * The iterator returns indexes in their natural order.
+ *
+ * @return an iterator over the enabled indexes
+ */
+ private PrimitiveIterator.OfInt iterator() {
+ return new IndexIterator();
+ }
+
+ @Override
+ public void merge(final BloomFilter other) {
+ applyAsBloomFilter(other, this::increment);
+ }
+
+ @Override
+ public void merge(final Hasher hasher) {
+ applyAsHasher(hasher, this::increment);
+ }
+
+ @Override
+ public boolean remove(BloomFilter other) {
+ applyAsBloomFilter(other, this::decrement);
+ return isValid();
+ }
+
+ @Override
+ public boolean remove(Hasher hasher) {
+ applyAsHasher(hasher, this::decrement);
+ return isValid();
+ }
+
+ @Override
+ public boolean add(CountingBloomFilter other) {
+ applyAsCountingBloomFilter(other, this::add);
+ return isValid();
+ }
+
+ @Override
+ public boolean subtract(CountingBloomFilter other) {
+ applyAsCountingBloomFilter(other, this::subtract);
+ return isValid();
+ }
+
+ /**
+ * {@inheritDoc}
+ *
+ * <p><em>Implementation note</em>
+ *
+ * <p>The state transition to invalid is permanent.
+ *
+ * <p>This implementation does not correct negative counts to zero or integer
+ * overflow counts to {@link Integer#MAX_VALUE}. Thus the operation that
+ * generated invalid counts can be reversed by using the complement of the
+ * original operation with the same Bloom filter. This will restore the counts
+ * to the state prior to the invalid operation. Counts can then be extracted
+ * using {@link #forEachCount(BitCountConsumer)}.
+ */
+ @Override
+ public boolean isValid() {
+ return state >= 0;
+ }
+
+ @Override
+ public void forEachCount(BitCountConsumer action) {
+ for (int i = 0; i < counts.length; i++) {
+ if (counts[i] != 0) {
+ action.accept(i, counts[i]);
+ }
+ }
+ }
+
+ /**
+ * Apply the action for each index in the Bloom filter.
+ */
+ private void applyAsBloomFilter(final BloomFilter other, final IntConsumer action) {
+ verifyShape(other);
+ if (other instanceof ArrayCountingBloomFilter) {
+ // Only use the presence of non-zero and not the counts
+ final int[] counts2 = ((ArrayCountingBloomFilter) other).counts;
+ for (int i = 0; i < counts2.length; i++) {
+ if (counts2[i] != 0) {
+ action.accept(i);
+ }
+ }
+ } else {
+ BitSet.valueOf(other.getBits()).stream().forEach(action);
+ }
+ }
+
+ /**
+ * Apply the action for each index in the hasher.
+ */
+ private void applyAsHasher(final Hasher hasher, final IntConsumer action) {
+ verifyHasher(hasher);
+ toSet(hasher).forEach(i -> action.accept(i));
Review comment:
Rather than toSet() how about a filtering consumer. Something like:
```java
private static class UniqueFilter implements IntConsumer {
IntConsumer child;
long[] filter;
UniqueFilter( IntConsumer child, Shape shape ) {
this.child=child;
filter = new long[ BloomFilterIndexer.getLongIndex(shape.getNumberOfBits()) ];
}
@Override
public void accept(int arg0) {
long target = filter[BloomFilterIndexer.getLongIndex(arg0) ];
long mask = BloomFilterIndexer.getLongBit(arg0);
if ( (target & mask) == 0)
{
target |= mask;
child.accept( arg0 );
}
}
}
```
the use
```java
UniqueFilter filter = new UniqueFilter( action, shape );
hasher.getBits(shape).forEachRemaining( filter );
```
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