| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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 org.apache.bookkeeper.client; |
| |
| import java.util.ArrayList; |
| import java.util.Collections; |
| import java.util.Comparator; |
| import java.util.HashMap; |
| import java.util.List; |
| import java.util.Map; |
| import java.util.TreeMap; |
| import java.util.concurrent.locks.ReadWriteLock; |
| import java.util.concurrent.locks.ReentrantReadWriteLock; |
| |
| import org.slf4j.Logger; |
| import org.slf4j.LoggerFactory; |
| |
| class WeightedRandomSelection<T> { |
| static final Logger LOG = LoggerFactory.getLogger(WeightedRandomSelection.class); |
| |
| interface WeightedObject { |
| long getWeight(); |
| } |
| Double randomMax; |
| int maxProbabilityMultiplier; |
| Map<T, WeightedObject> map; |
| TreeMap<Double, T> cummulativeMap = new TreeMap<Double, T>(); |
| ReadWriteLock rwLock = new ReentrantReadWriteLock(true); |
| |
| WeightedRandomSelection() { |
| maxProbabilityMultiplier = -1; |
| } |
| |
| WeightedRandomSelection(int maxMultiplier) { |
| this.maxProbabilityMultiplier = maxMultiplier; |
| } |
| |
| public void setMaxProbabilityMultiplier(int max) { |
| this.maxProbabilityMultiplier = max; |
| } |
| |
| void updateMap(Map<T, WeightedObject> map) { |
| // get the sum total of all the values; this will be used to |
| // calculate the weighted probability later on |
| Long totalWeight = 0L, min = Long.MAX_VALUE; |
| List<WeightedObject> values = new ArrayList<WeightedObject>(map.values()); |
| Collections.sort(values, new Comparator<WeightedObject>() { |
| public int compare(WeightedObject o1, WeightedObject o2) { |
| long diff = o1.getWeight() - o2.getWeight(); |
| if (diff < 0L) { |
| return -1; |
| } else if (diff > 0L) { |
| return 1; |
| } else { |
| return 0; |
| } |
| } |
| }); |
| for (int i = 0; i < values.size(); i++) { |
| totalWeight += values.get(i).getWeight(); |
| if (values.get(i).getWeight() != 0 && min > values.get(i).getWeight()) { |
| min = values.get(i).getWeight(); |
| } |
| } |
| |
| double median = 0; |
| if (totalWeight == 0) { |
| // all the values are zeros; assign a value of 1 to all and the totalWeight equal |
| // to the size of the values |
| min = 1L; |
| median = 1; |
| totalWeight = (long) values.size(); |
| } else { |
| int mid = values.size() / 2; |
| if ((values.size() % 2) == 1) { |
| median = values.get(mid).getWeight(); |
| } else { |
| median = (double) (values.get(mid - 1).getWeight() + values.get(mid).getWeight()) / 2; |
| } |
| } |
| |
| double medianWeight, minWeight; |
| medianWeight = median / (double) totalWeight; |
| minWeight = (double) min / totalWeight; |
| |
| if (LOG.isDebugEnabled()) { |
| LOG.debug("Updating weights map. MediaWeight: {} MinWeight: {}", medianWeight, minWeight); |
| } |
| |
| double maxWeight = maxProbabilityMultiplier * medianWeight; |
| Map<T, Double> weightMap = new HashMap<T, Double>(); |
| for (Map.Entry<T, WeightedObject> e : map.entrySet()) { |
| double weightedProbability; |
| if (e.getValue().getWeight() > 0) { |
| weightedProbability = (double) e.getValue().getWeight() / (double) totalWeight; |
| } else { |
| weightedProbability = minWeight; |
| } |
| if (maxWeight > 0 && weightedProbability > maxWeight) { |
| weightedProbability = maxWeight; |
| if (LOG.isDebugEnabled()) { |
| LOG.debug("Capping the probability to {} for {} Value: {}", |
| weightedProbability, e.getKey(), e.getValue()); |
| } |
| } |
| weightMap.put(e.getKey(), weightedProbability); |
| } |
| |
| // The probability of picking a bookie randomly is defaultPickProbability |
| // but we change that priority by looking at the weight that each bookie |
| // carries. |
| TreeMap<Double, T> tmpCummulativeMap = new TreeMap<Double, T>(); |
| Double key = 0.0; |
| for (Map.Entry<T, Double> e : weightMap.entrySet()) { |
| tmpCummulativeMap.put(key, e.getKey()); |
| if (LOG.isDebugEnabled()) { |
| LOG.debug("Key: {} Value: {} AssignedKey: {} AssignedWeight: {}", |
| e.getKey(), e.getValue(), key, e.getValue()); |
| } |
| key += e.getValue(); |
| } |
| |
| rwLock.writeLock().lock(); |
| try { |
| this.map = map; |
| cummulativeMap = tmpCummulativeMap; |
| randomMax = key; |
| } finally { |
| rwLock.writeLock().unlock(); |
| } |
| } |
| |
| T getNextRandom() { |
| rwLock.readLock().lock(); |
| try { |
| // pick a random number between 0 and randMax |
| Double randomNum = randomMax * Math.random(); |
| // find the nearest key in the map corresponding to the randomNum |
| Double key = cummulativeMap.floorKey(randomNum); |
| //LOG.info("Random max: {} CummulativeMap size: {} selected key: {}", randomMax, cummulativeMap.size(), |
| // key); |
| return cummulativeMap.get(key); |
| } finally { |
| rwLock.readLock().unlock(); |
| } |
| } |
| } |