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
17package com.google.common.collect;
18
19import com.google.caliper.AfterExperiment;
20import com.google.caliper.BeforeExperiment;
21import com.google.caliper.Benchmark;
22import com.google.caliper.Param;
23import com.google.common.base.Function;
24import com.google.common.collect.MapMaker;
25import com.google.common.primitives.Ints;
26
27import java.util.Map;
28import java.util.Random;
29import 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 */
36public 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