| package org.apache.helix.controller.rebalancer.waged.constraints; |
| |
| /* |
| * 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. |
| */ |
| |
| import org.apache.helix.controller.rebalancer.waged.model.AssignableNode; |
| import org.apache.helix.controller.rebalancer.waged.model.AssignableReplica; |
| import org.apache.helix.controller.rebalancer.waged.model.ClusterContext; |
| |
| /** |
| * The "soft" constraint evaluates the optimality of an assignment by giving it a score of a scale of [minScore, maxScore] |
| * The higher the score, the better the assignment; Intuitively, the assignment is encouraged. |
| * The lower score the score, the worse the assignment; Intuitively, the assignment is penalized. |
| */ |
| abstract class SoftConstraint { |
| private float _maxScore = 1000f; |
| private float _minScore = -1000f; |
| |
| interface NormalizeFunction { |
| /** |
| * Scale the origin score to a normalized range (0, 1). |
| * The purpose is to compare scores between different soft constraints. |
| * @param originScore The origin score |
| * @return The normalized value between (0, 1) |
| */ |
| float scale(float originScore); |
| } |
| |
| /** |
| * Default constructor, uses default min/max scores |
| */ |
| SoftConstraint() { |
| } |
| |
| /** |
| * Child class customize the min/max score on its own |
| * @param maxScore The max score |
| * @param minScore The min score |
| */ |
| SoftConstraint(float maxScore, float minScore) { |
| _maxScore = maxScore; |
| _minScore = minScore; |
| } |
| |
| float getMaxScore() { |
| return _maxScore; |
| } |
| |
| float getMinScore() { |
| return _minScore; |
| } |
| |
| /** |
| * Evaluate and give a score for an potential assignment partition -> instance |
| * Child class only needs to care about how the score is implemented |
| * @return The score of the assignment in float value |
| */ |
| protected abstract float getAssignmentScore(AssignableNode node, AssignableReplica replica, |
| ClusterContext clusterContext); |
| |
| /** |
| * Evaluate and give a score for an potential assignment partition -> instance |
| * It's the only exposed method to the caller |
| * @return The score is normalized to be within MinScore and MaxScore |
| */ |
| float getAssignmentNormalizedScore(AssignableNode node, AssignableReplica replica, |
| ClusterContext clusterContext) { |
| return getNormalizeFunction().scale(getAssignmentScore(node, replica, clusterContext)); |
| } |
| |
| /** |
| * The default scaler function that squashes any score within (min_score, max_score) to (0, 1); |
| * Child class could override the method and customize the method on its own |
| * @return The MinMaxScaler instance by default |
| */ |
| NormalizeFunction getNormalizeFunction() { |
| return (score) -> (score - getMinScore()) / (getMaxScore() - getMinScore()); |
| } |
| } |