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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.collect;
     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.base.Function;
     24 import com.google.common.collect.MapMaker;
     25 import com.google.common.primitives.Ints;
     26 
     27 import java.util.Map;
     28 import java.util.Random;
     29 import java.util.concurrent.atomic.AtomicLong;
     30 
     31 /**
     32  * Simple single-threaded benchmark for a computing map with maximum size.
     33  *
     34  * @author Charles Fry
     35  */
     36 public class MapMakerSingleThreadBenchmark {
     37   @Param({"1000", "2000"}) int maximumSize;
     38   @Param("5000") int distinctKeys;
     39   @Param("4") int segments;
     40 
     41   // 1 means uniform likelihood of keys; higher means some keys are more popular
     42   // tweak this to control hit rate
     43   @Param("2.5") double concentration;
     44 
     45   Random random = new Random();
     46 
     47   Map<Integer, Integer> cache;
     48 
     49   int max;
     50 
     51   static AtomicLong requests = new AtomicLong(0);
     52   static AtomicLong misses = new AtomicLong(0);
     53 
     54   @BeforeExperiment void setUp() {
     55     // random integers will be generated in this range, then raised to the
     56     // power of (1/concentration) and floor()ed
     57     max = Ints.checkedCast((long) Math.pow(distinctKeys, concentration));
     58 
     59     cache = new MapMaker()
     60         .concurrencyLevel(segments)
     61         .maximumSize(maximumSize)
     62         .makeComputingMap(
     63             new Function<Integer, Integer>() {
     64               @Override public Integer apply(Integer from) {
     65                 return (int) misses.incrementAndGet();
     66               }
     67             });
     68 
     69     // To start, fill up the cache.
     70     // Each miss both increments the counter and causes the map to grow by one,
     71     // so until evictions begin, the size of the map is the greatest return
     72     // value seen so far
     73     while (cache.get(nextRandomKey()) < maximumSize) {}
     74 
     75     requests.set(0);
     76     misses.set(0);
     77   }
     78 
     79   @Benchmark int time(int reps) {
     80     int dummy = 0;
     81     for (int i = 0; i < reps; i++) {
     82       dummy += cache.get(nextRandomKey());
     83     }
     84     requests.addAndGet(reps);
     85     return dummy;
     86   }
     87 
     88   private int nextRandomKey() {
     89     int a = random.nextInt(max);
     90 
     91     /*
     92      * For example, if concentration=2.0, the following takes the square root of
     93      * the uniformly-distributed random integer, then truncates any fractional
     94      * part, so higher integers would appear (in this case linearly) more often
     95      * than lower ones.
     96      */
     97     return (int) Math.pow(a, 1.0 / concentration);
     98   }
     99 
    100   @AfterExperiment void tearDown() {
    101     double req = requests.get();
    102     double hit = req - misses.get();
    103 
    104     // Currently, this is going into /dev/null, but I'll fix that
    105     System.out.println("hit rate: " + hit / req);
    106   }
    107 
    108   // for proper distributions later:
    109   // import JSci.maths.statistics.ProbabilityDistribution;
    110   // int key = (int) dist.inverse(random.nextDouble());
    111 }
    112