Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/10883#discussion_r50761798
--- Diff:
common/sketch/src/main/java/org/apache/spark/util/sketch/BloomFilter.java ---
@@ -0,0 +1,151 @@
+/*
+ * 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.spark.util.sketch;
+
+/**
+ * A Bloom filter is a space-efficient probabilistic data structure, that
is used to test whether
+ * an element is a member of a set. It returns false when the element is
definitely not in the
+ * set, returns true when the element is probably in the set.
+ *
+ * Internally a Bloom filter is initialized with 2 information: how many
space to use(number of
+ * bits) and how many hash values to calculate for each record. To get as
lower false positive
+ * probability as possible, user should call {@link BloomFilter#create} to
automatically pick a
+ * best combination of these 2 parameters.
+ *
+ * Currently the following data types are supported:
+ * <ul>
+ * <li>{@link Byte}</li>
+ * <li>{@link Short}</li>
+ * <li>{@link Integer}</li>
+ * <li>{@link Long}</li>
+ * <li>{@link String}</li>
+ * </ul>
+ *
+ * The implementation is largely based on the {@code BloomFilter} class
from guava.
+ */
+public abstract class BloomFilter {
+ /**
+ * Returns the false positive probability, i.e. the probability that
+ * {@linkplain #mightContain(Object)} will erroneously return {@code
true} for an object that
+ * has not actually been put in the {@code BloomFilter}.
+ *
+ * <p>Ideally, this number should be close to the {@code fpp} parameter
+ * passed in to create this bloom filter, or smaller. If it is
+ * significantly higher, it is usually the case that too many elements
(more than
+ * expected) have been put in the {@code BloomFilter}, degenerating it.
+ */
+ public abstract double expectedFpp();
+
+ /**
+ * Returns the number of bits in the underlying bit array.
+ */
+ public abstract long bitSize();
+
+ /**
+ * Puts an element into this {@code BloomFilter}. Ensures that
subsequent invocations of
+ * {@link #mightContain(Object)} with the same element will always
return {@code true}.
+ *
+ * @return true if the bloom filter's bits changed as a result of this
operation. If the bits
+ * changed, this is <i>definitely</i> the first time {@code object}
has been added to the
+ * filter. If the bits haven't changed, this <i>might</i> be the
first time {@code object}
+ * has been added to the filter. Note that {@code put(t)} always
returns the
+ * <i>opposite</i> result to what {@code mightContain(t)} would have
returned at the time
+ * it is called.
+ */
+ public abstract boolean put(Object item);
+
+ /**
+ * Determines whether a given bloom filter is compatible with this bloom
filter. For two
+ * bloom filters to be compatible, they must have the same bit size.
+ *
+ * @param other The bloom filter to check for compatibility.
+ */
+ public abstract boolean isCompatible(BloomFilter other);
+
+ /**
+ * Combines this bloom filter with another bloom filter by performing a
bitwise OR of the
+ * underlying data. The mutations happen to <b>this</b> instance.
Callers must ensure the
+ * bloom filters are appropriately sized to avoid saturating them.
+ *
+ * @param other The bloom filter to combine this bloom filter with. It
is not mutated.
+ * @throws IllegalArgumentException if {@code isCompatible(that) ==
false}
+ */
+ public abstract BloomFilter mergeInPlace(BloomFilter other);
+
+ /**
+ * Returns {@code true} if the element <i>might</i> have been put in
this Bloom filter,
+ * {@code false} if this is <i>definitely</i> not the case.
+ */
+ public abstract boolean mightContain(Object item);
+
+ /**
+ * Computes the optimal k (number of hashes per element inserted in
Bloom filter), given the
+ * expected insertions and total number of bits in the Bloom filter.
+ *
+ * See http://en.wikipedia.org/wiki/File:Bloom_filter_fp_probability.svg
for the formula.
+ *
+ * @param n expected insertions (must be positive)
+ * @param m total number of bits in Bloom filter (must be positive)
+ */
+ private static int optimalNumOfHashFunctions(long n, long m) {
+ // (m / n) * log(2), but avoid truncation due to division!
+ return Math.max(1, (int) Math.round((double) m / n * Math.log(2)));
+ }
+
+ /**
+ * Computes m (total bits of Bloom filter) which is expected to achieve,
for the specified
+ * expected insertions, the required false positive probability.
+ *
+ * See
http://en.wikipedia.org/wiki/Bloom_filter#Probability_of_false_positives for
the formula.
+ *
+ * @param n expected insertions (must be positive)
+ * @param p false positive rate (must be 0 < p < 1)
+ */
+ private static long optimalNumOfBits(long n, double p) {
+ return (long) (-n * Math.log(p) / (Math.log(2) * Math.log(2)));
+ }
+
+ /**
+ * Creates a {@link BloomFilter} with given {@code expectedNumItems} and
a default 3% {@code fpp}.
+ */
+ public static BloomFilter create(long expectedNumItems) {
+ return create(expectedNumItems, 0.03);
--- End diff --
Maybe we should have a constant `DEFAULT_EXPECTED_FPP` for this 0.03. I was
pretty confused when finding the magic number 0.03 in the testing code.
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