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      1 /*
      2  * Copyright (C) 2010 The Guava Authors
      3  *
      4  * Licensed under the Apache License, Version 2.0 (the "License");
      5  * you may not use this file except in compliance with the License.
      6  * You may obtain a copy of the License at
      7  *
      8  * http://www.apache.org/licenses/LICENSE-2.0
      9  *
     10  * Unless required by applicable law or agreed to in writing, software
     11  * distributed under the License is distributed on an "AS IS" BASIS,
     12  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     13  * See the License for the specific language governing permissions and
     14  * limitations under the License.
     15  */
     16 
     17 package com.google.common.cache;
     18 
     19 import com.google.caliper.AfterExperiment;
     20 import com.google.caliper.BeforeExperiment;
     21 import com.google.caliper.Benchmark;
     22 import com.google.caliper.Param;
     23 import com.google.common.primitives.Ints;
     24 
     25 import java.util.Random;
     26 import java.util.concurrent.atomic.AtomicLong;
     27 
     28 /**
     29  * Single-threaded benchmark for {@link LoadingCache}.
     30  *
     31  * @author Charles Fry
     32  */
     33 public class LoadingCacheSingleThreadBenchmark {
     34   @Param({"1000", "2000"}) int maximumSize;
     35   @Param("5000") int distinctKeys;
     36   @Param("4") int segments;
     37 
     38   // 1 means uniform likelihood of keys; higher means some keys are more popular
     39   // tweak this to control hit rate
     40   @Param("2.5") double concentration;
     41 
     42   Random random = new Random();
     43 
     44   LoadingCache<Integer, Integer> cache;
     45 
     46   int max;
     47 
     48   static AtomicLong requests = new AtomicLong(0);
     49   static AtomicLong misses = new AtomicLong(0);
     50 
     51   @BeforeExperiment void setUp() {
     52     // random integers will be generated in this range, then raised to the
     53     // power of (1/concentration) and floor()ed
     54     max = Ints.checkedCast((long) Math.pow(distinctKeys, concentration));
     55 
     56     cache = CacheBuilder.newBuilder()
     57         .concurrencyLevel(segments)
     58         .maximumSize(maximumSize)
     59         .build(
     60             new CacheLoader<Integer, Integer>() {
     61               @Override public Integer load(Integer from) {
     62                 return (int) misses.incrementAndGet();
     63               }
     64             });
     65 
     66     // To start, fill up the cache.
     67     // Each miss both increments the counter and causes the map to grow by one,
     68     // so until evictions begin, the size of the map is the greatest return
     69     // value seen so far
     70     while (cache.getUnchecked(nextRandomKey()) < maximumSize) {}
     71 
     72     requests.set(0);
     73     misses.set(0);
     74   }
     75 
     76   @Benchmark int time(int reps) {
     77     int dummy = 0;
     78     for (int i = 0; i < reps; i++) {
     79       dummy += cache.getUnchecked(nextRandomKey());
     80     }
     81     requests.addAndGet(reps);
     82     return dummy;
     83   }
     84 
     85   private int nextRandomKey() {
     86     int a = random.nextInt(max);
     87 
     88     /*
     89      * For example, if concentration=2.0, the following takes the square root of
     90      * the uniformly-distributed random integer, then truncates any fractional
     91      * part, so higher integers would appear (in this case linearly) more often
     92      * than lower ones.
     93      */
     94     return (int) Math.pow(a, 1.0 / concentration);
     95   }
     96 
     97   @AfterExperiment void tearDown() {
     98     double req = requests.get();
     99     double hit = req - misses.get();
    100 
    101     // Currently, this is going into /dev/null, but I'll fix that
    102     System.out.println("hit rate: " + hit / req);
    103   }
    104 
    105   // for proper distributions later:
    106   // import JSci.maths.statistics.ProbabilityDistribution;
    107   // int key = (int) dist.inverse(random.nextDouble());
    108 }
    109