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Remove the simulator's split package for Jigsaw modularity
The CountMin64 implementation is provided by AddThis StreamLib, but required modifying the package-private state for the TinyLFU reset operation. This was done by using the same package name for field access, but will break in JDK9 modularity restrictions. Now the CountMin64 is a slimmed down fork of the original code. The BloomFilter option was removed (since 2 others are provided) so that the dependency could be removed. The JavaDoc retains the attribution.
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104 changes: 104 additions & 0 deletions
104
...in/java/com/github/benmanes/caffeine/cache/simulator/admission/countmin64/CountMin64.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| /* | ||
| * Licensed 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 com.github.benmanes.caffeine.cache.simulator.admission.countmin64; | ||
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| import static com.google.common.base.Preconditions.checkArgument; | ||
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| import java.util.Random; | ||
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| /** | ||
| * Count-Min Sketch data structure with optional conservative addition. | ||
| * <p> | ||
| * This is a derived from <tt>CountMinSketch</tt> and <tt>ConservativeAddSketch</tt> provided by | ||
| * <a href="https://github.com/addthis/stream-lib">StreamLib</a>. | ||
| */ | ||
| final class CountMin64 { | ||
| static final long PRIME_MODULUS = (1L << 31) - 1; | ||
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| final long[][] table; | ||
| final long[] hashA; | ||
| final int depth; | ||
| final int width; | ||
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| public CountMin64(double eps, double confidence, int seed) { | ||
| // 2/w = eps ; w = 2/eps | ||
| // 1/2^depth <= 1-confidence ; depth >= -log2 (1-confidence) | ||
| this.width = (int) Math.ceil(2 / eps); | ||
| this.depth = (int) Math.ceil(-Math.log(1 - confidence) / Math.log(2)); | ||
| this.table = new long[depth][width]; | ||
| this.hashA = new long[depth]; | ||
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| // We're using a linear hash functions of the form ((a*x+b) mod p) where a,b are chosen | ||
| // independently for each hash function. However we can set b = 0 as all it does is shift the | ||
| // results without compromising their uniformity or independence with the other hashes. | ||
| Random r = new Random(seed); | ||
| for (int i = 0; i < depth; ++i) { | ||
| hashA[i] = r.nextInt(Integer.MAX_VALUE); | ||
| } | ||
| } | ||
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| /** The estimate is correct within epsilon * (total item count), with probability confidence. */ | ||
| public long estimateCount(long item) { | ||
| long count = Long.MAX_VALUE; | ||
| for (int i = 0; i < depth; ++i) { | ||
| count = Math.min(count, table[i][hash(item, i)]); | ||
| } | ||
| return count; | ||
| } | ||
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| public void add(boolean conservative, long item, long count) { | ||
| // Actually for negative increments we'll need to use the median instead of minimum, and | ||
| // accuracy will suffer somewhat. Probably makes sense to add an "allow negative increments" | ||
| // parameter to constructor. | ||
| checkArgument(count >= 0, "Negative increments not implemented"); | ||
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| if (conservative) { | ||
| conservativeAdd(item, count); | ||
| } else { | ||
| add(item, count); | ||
| } | ||
| } | ||
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| private void add(long item, long count) { | ||
| for (int i = 0; i < depth; ++i) { | ||
| table[i][hash(item, i)] += count; | ||
| } | ||
| } | ||
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| private void conservativeAdd(long item, long count) { | ||
| int[] buckets = new int[depth]; | ||
| for (int i = 0; i < depth; ++i) { | ||
| buckets[i] = hash(item, i); | ||
| } | ||
| long min = table[0][buckets[0]]; | ||
| for (int i = 1; i < depth; ++i) { | ||
| min = Math.min(min, table[i][buckets[i]]); | ||
| } | ||
| for (int i = 0; i < depth; ++i) { | ||
| long newVal = Math.max(table[i][buckets[i]], min + count); | ||
| table[i][buckets[i]] = newVal; | ||
| } | ||
| } | ||
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| private int hash(long item, int i) { | ||
| long hash = hashA[i] * item; | ||
| // A super fast way of computing x mod 2^p-1 | ||
| // See http://www.cs.princeton.edu/courses/archive/fall09/cos521/Handouts/universalclasses.pdf | ||
| // page 149, right after Proposition 7. | ||
| hash += hash >> 32; | ||
| hash &= PRIME_MODULUS; | ||
| // Doing "%" after (int) conversion is ~2x faster than %'ing longs. | ||
| return ((int) hash) % width; | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -16,7 +16,6 @@ | |
| package com.github.benmanes.caffeine.cache.simulator.membership; | ||
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| import com.github.benmanes.caffeine.cache.simulator.BasicSettings; | ||
| import com.github.benmanes.caffeine.cache.simulator.membership.bloom.AddThisBloomFilter; | ||
| import com.github.benmanes.caffeine.cache.simulator.membership.bloom.BloomFilter; | ||
| import com.github.benmanes.caffeine.cache.simulator.membership.bloom.GuavaBloomFilter; | ||
| import com.typesafe.config.Config; | ||
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@@ -27,14 +26,6 @@ | |
| * @author [email protected] (Ben Manes) | ||
| */ | ||
| public enum FilterType { | ||
| ADDTHIS { | ||
| @Override public Membership create(long expectedInsertions, double fpp, Config config) { | ||
| return new AddThisBloomFilter(expectedInsertions, fpp); | ||
| } | ||
| @Override public String toString() { | ||
| return "AddThis"; | ||
| } | ||
| }, | ||
| CAFFEINE { | ||
| @Override public Membership create(long expectedInsertions, double fpp, Config config) { | ||
| int randomSeed = new BasicSettings(config).randomSeed(); | ||
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51 changes: 0 additions & 51 deletions
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...ava/com/github/benmanes/caffeine/cache/simulator/membership/bloom/AddThisBloomFilter.java
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