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This is the ehcache core module. Pair it with other modules for added functionality.
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/**
 *  Copyright 2003-2010 Terracotta, Inc.
 *
 *  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 net.sf.ehcache.store;

import net.sf.ehcache.Element;

import java.util.Random;

/**
 * A base policy class
 *
 * @author Greg Luck
 */
public abstract class AbstractPolicy implements Policy {

    /**
     * The sample size to use
     */
    static final int DEFAULT_SAMPLE_SIZE = 15;

    /**
     * Used to select random numbers
     */
    static final Random RANDOM = new Random();

    /**
     * sampleSize how many samples to take
     *
     * @param populationSize the size of the store
     * @return the smaller of the map size and the default sample size of 30
     */
    public static int calculateSampleSize(int populationSize) {
        if (populationSize < DEFAULT_SAMPLE_SIZE) {
            return populationSize;
        } else {
            return DEFAULT_SAMPLE_SIZE;
        }
    }

    /**
     * Finds the best eviction candidate based on the sampled elements. What distuingishes this approach
     * from the classic data structures approach, is that an Element contains metadata which can be used
     * for making policy decisions, while generic data structures do not.
     *
     * @param sampledElements this should be a random subset of the population
     * @param justAdded       we never want to select the element just added. May be null.
     * @return the least hit
     */
    public Element selectedBasedOnPolicy(Element[] sampledElements, Element justAdded) {
        //edge condition when Memory Store configured to size 0
        if (sampledElements.length == 1 && justAdded != null) {
            return justAdded;
        }
        Element lowestElement = null;
        for (Element element : sampledElements) {
            if (element == null) {
                continue;
            }
            if (lowestElement == null) {
                if (!element.equals(justAdded)) {
                    lowestElement = element;
                }
            } else if (compare(lowestElement, element) && !element.equals(justAdded)) {
                lowestElement = element;
            }

        }
        return lowestElement;
    }

    /**
     * Generates a random sample from a population
     *
     * @param populationSize the size to draw from
     * @return a list of random offsets
     */
    public static int[] generateRandomSample(int populationSize) {
        int sampleSize = calculateSampleSize(populationSize);
        int[] offsets = new int[sampleSize];

        //Guard against the possibility (which can happen) that the store has emptied, via another thread(s) and thus sampleSize is 0.
        //Otherwise return an empty array.
        if (sampleSize != 0) {
            int maxOffset = 0;
            maxOffset = populationSize / sampleSize;
            for (int i = 0; i < sampleSize; i++) {
                offsets[i] = RANDOM.nextInt(maxOffset);
            }
        }
        return offsets;
    }
}