* Copyright (C) 2010 The Guava Authors
 * 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,
 * See the License for the specific language governing permissions and
 * limitations under the License.

package com.google.common.cache;

import com.google.caliper.AfterExperiment;
import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import com.google.common.primitives.Ints;
import java.util.Random;
import java.util.concurrent.atomic.AtomicLong;

 * Single-threaded benchmark for {@link LoadingCache}.
 * @author Charles Fry
public class LoadingCacheSingleThreadBenchmark {
  @Param({"1000", "2000"}) int maximumSize;
  @Param("5000") int distinctKeys;
  @Param("4") int segments;

  // 1 means uniform likelihood of keys; higher means some keys are more popular
  // tweak this to control hit rate
  @Param("2.5") double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment void setUp() {
    // random integers will be generated in this range, then raised to the
    // power of (1/concentration) and floor()ed
    max = Ints.checkedCast((long) Math.pow(distinctKeys, concentration));

    cache = CacheBuilder.newBuilder()
            new CacheLoader<Integer, Integer>() {
              @Override public Integer load(Integer from) {
                return (int) misses.incrementAndGet();

    // To start, fill up the cache.
    // Each miss both increments the counter and causes the map to grow by one,
    // so until evictions begin, the size of the map is the greatest return
    // value seen so far
    while (cache.getUnchecked(nextRandomKey()) < maximumSize) {}


  @Benchmark int time(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += cache.getUnchecked(nextRandomKey());
    return dummy;

  private int nextRandomKey() {
    int a = random.nextInt(max);

     * For example, if concentration=2.0, the following takes the square root of
     * the uniformly-distributed random integer, then truncates any fractional
     * part, so higher integers would appear (in this case linearly) more often
     * than lower ones.
    return (int) Math.pow(a, 1.0 / concentration);

  @AfterExperiment void tearDown() {
    double req = requests.get();
    double hit = req - misses.get();

    // Currently, this is going into /dev/null, but I'll fix that
    System.out.println("hit rate: " + hit / req);

  // for proper distributions later:
  // import JSci.maths.statistics.ProbabilityDistribution;
  // int key = (int) dist.inverse(random.nextDouble());