/*
* Copyright 2015 Gilga Einziger. All Rights Reserved.
*
* 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.tinycache;
import com.github.benmanes.caffeine.cache.simulator.BasicSettings;
import com.github.benmanes.caffeine.cache.simulator.admission.Frequency;
import com.typesafe.config.Config;
/**
* The TinyCache admission policy.
*
* @author gilga1983@gmail.com (Gil Einziger)
*/
public final class TinyCacheAdapter implements Frequency {
// the actual data structure.
final TinyCacheSketch tcs;
// size between cache and sample.
static final int sampleFactor = 10;
// max frequency estimation of an item.
static final int maxcount = 10;
/**
* Note that in this implementation there are always 64 items per set.
*/
public TinyCacheAdapter(Config config) {
BasicSettings settings = new BasicSettings(config);
int nrSets = sampleFactor * settings.maximumSize() / 64; // number of (independent sets)
tcs = new TinyCacheSketch(nrSets, 64,settings.randomSeed());
}
@Override
public int frequency(long e) {
return tcs.countItem(e);
}
@Override
public void increment(long e) {
if (tcs.countItem(e) < maxcount) {
tcs.addItem(e);
}
}
}