001/*
002 * Licensed to the Apache Software Foundation (ASF) under one
003 * or more contributor license agreements.  See the NOTICE file
004 * distributed with this work for additional information
005 * regarding copyright ownership.  The ASF licenses this file
006 * to you under the Apache License, Version 2.0 (the
007 * "License"); you may not use this file except in compliance
008 * with the License.  You may obtain a copy of the License at
009 *
010 *     http://www.apache.org/licenses/LICENSE-2.0
011 *
012 * Unless required by applicable law or agreed to in writing, software
013 * distributed under the License is distributed on an "AS IS" BASIS,
014 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
015 * See the License for the specific language governing permissions and
016 * limitations under the License.
017 */
018package org.apache.hadoop.hbase.util;
019
020import org.apache.yetus.audience.InterfaceAudience;
021
022/**
023 * EMA is similar to {@link WeightedMovingAverage} in weighted, but the weighting factor decrease
024 * exponentially. It brings benefits that it is more sensitive, and can see the trends easily.
025 */
026@InterfaceAudience.Private
027public class ExponentialMovingAverage<T> extends WindowMovingAverage<T> {
028  private double alpha;
029  private double previousAverage;
030  private double currentAverage;
031
032  public ExponentialMovingAverage(String label) {
033    this(label, DEFAULT_SIZE);
034  }
035
036  public ExponentialMovingAverage(String label, double alpha) {
037    this(label, DEFAULT_SIZE, alpha);
038  }
039
040  public ExponentialMovingAverage(String label, int size) {
041    this(label, size, (double) 2 / (1 + size));
042  }
043
044  public ExponentialMovingAverage(String label, int size, double alpha) {
045    super(label, size);
046    this.previousAverage = -1.0;
047    this.currentAverage = 0.0;
048    this.alpha = alpha;
049  }
050
051  @Override
052  public void updateMostRecentTime(long elapsed) {
053    if (!enoughStatistics()) {
054      previousAverage = super.getAverageTime();
055      super.updateMostRecentTime(elapsed);
056      if (!enoughStatistics()) {
057        return;
058      }
059    }
060    // CurrentEMA = α * currentValue + (1 - α) * previousEMA =>
061    // CurrentEMA = (currentValue - previousEMA) * α + previousEMA
062    // This will reduce multiplication.
063    currentAverage = (elapsed - previousAverage) * alpha + previousAverage;
064    previousAverage = currentAverage;
065  }
066
067  @Override
068  public double getAverageTime() {
069    if (!enoughStatistics()) {
070      return super.getAverageTime();
071    }
072    return currentAverage;
073  }
074
075  double getPrevious() {
076    return previousAverage;
077  }
078}