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Java example source code file (LoadingCacheSingleThreadBenchmark.java)

This example Java source code file (LoadingCacheSingleThreadBenchmark.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

afterexperiment, atomiclong, beforeexperiment, benchmark, cacheloader, integer, loadingcache, loadingcachesinglethreadbenchmark, override, param, random, util

The LoadingCacheSingleThreadBenchmark.java Java example source code

/*
 * 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,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * 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()
        .concurrencyLevel(segments)
        .maximumSize(maximumSize)
        .build(
            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) {}

    requests.set(0);
    misses.set(0);
  }

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

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