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 import java.util.Random;
25 import java.util.concurrent.atomic.AtomicLong;
26 
27 /**
28  * Single-threaded benchmark for {@link LoadingCache}.
29  *
30  * @author Charles Fry
31  */
32 public class LoadingCacheSingleThreadBenchmark {
33   @Param({"1000", "2000"})
34   int maximumSize;
35 
36   @Param("5000")
37   int distinctKeys;
38 
39   @Param("4")
40   int segments;
41 
42   // 1 means uniform likelihood of keys; higher means some keys are more popular
43   // tweak this to control hit rate
44   @Param("2.5")
45   double concentration;
46 
47   Random random = new Random();
48 
49   LoadingCache<Integer, Integer> cache;
50 
51   int max;
52 
53   static AtomicLong requests = new AtomicLong(0);
54   static AtomicLong misses = new AtomicLong(0);
55 
56   @BeforeExperiment
setUp()57   void setUp() {
58     // random integers will be generated in this range, then raised to the
59     // power of (1/concentration) and floor()ed
60     max = Ints.checkedCast((long) Math.pow(distinctKeys, concentration));
61 
62     cache =
63         CacheBuilder.newBuilder()
64             .concurrencyLevel(segments)
65             .maximumSize(maximumSize)
66             .build(
67                 new CacheLoader<Integer, Integer>() {
68                   @Override
69                   public Integer load(Integer from) {
70                     return (int) misses.incrementAndGet();
71                   }
72                 });
73 
74     // To start, fill up the cache.
75     // Each miss both increments the counter and causes the map to grow by one,
76     // so until evictions begin, the size of the map is the greatest return
77     // value seen so far
78     while (cache.getUnchecked(nextRandomKey()) < maximumSize) {}
79 
80     requests.set(0);
81     misses.set(0);
82   }
83 
84   @Benchmark
time(int reps)85   int time(int reps) {
86     int dummy = 0;
87     for (int i = 0; i < reps; i++) {
88       dummy += cache.getUnchecked(nextRandomKey());
89     }
90     requests.addAndGet(reps);
91     return dummy;
92   }
93 
nextRandomKey()94   private int nextRandomKey() {
95     int a = random.nextInt(max);
96 
97     /*
98      * For example, if concentration=2.0, the following takes the square root of
99      * the uniformly-distributed random integer, then truncates any fractional
100      * part, so higher integers would appear (in this case linearly) more often
101      * than lower ones.
102      */
103     return (int) Math.pow(a, 1.0 / concentration);
104   }
105 
106   @AfterExperiment
tearDown()107   void tearDown() {
108     double req = requests.get();
109     double hit = req - misses.get();
110 
111     // Currently, this is going into /dev/null, but I'll fix that
112     System.out.println("hit rate: " + hit / req);
113   }
114 
115   // for proper distributions later:
116   // import JSci.maths.statistics.ProbabilityDistribution;
117   // int key = (int) dist.inverse(random.nextDouble());
118 }
119