hbase-common/src/main/java/org/apache/hadoop/hbase/util/ExponentialMovingAverage.java

   1 /*
   2  * Licensed to the Apache Software Foundation (ASF) under one
   3  * or more contributor license agreements.  See the NOTICE file
   4  * distributed with this work for additional information
   5  * regarding copyright ownership.  The ASF licenses this file
   6  * to you under the Apache License, Version 2.0 (the
   7  * "License"); you may not use this file except in compliance
   8  * with the License.  You may obtain a copy of the License at
   9  *
  10  *     http://www.apache.org/licenses/LICENSE-2.0
  11  *
  12  * Unless required by applicable law or agreed to in writing, software
  13  * distributed under the License is distributed on an "AS IS" BASIS,
  14  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  15  * See the License for the specific language governing permissions and
  16  * limitations under the License.
  17  */
  18
  19 package org.apache.hadoop.hbase.util;
  20
  21 import org.apache.yetus.audience.InterfaceAudience;
  22
  23 /**
  24  * EMA is similar to {@link WeightedMovingAverage} in weighted, but the weighting factor decrease
  25  * exponentially. It brings benefits that it is more sensitive, and can see the trends easily.
  26  */
  27 @InterfaceAudience.Private
  28 public class ExponentialMovingAverage extends WindowMovingAverage {
  29   private double alpha;
  30   private double previousAverage;
  31   private double currentAverage;
  32
  33   public ExponentialMovingAverage(String label) {
  34     this(label, DEFAULT_SIZE);
  35   }
  36
  37   public ExponentialMovingAverage(String label, double alpha) {
  38     this(label, DEFAULT_SIZE, alpha);
  39   }
  40
  41   public ExponentialMovingAverage(String label, int size) {
  42     this(label, size, (double) 2 / (1 + size));
  43   }
  44
  45   public ExponentialMovingAverage(String label, int size, double alpha) {
  46     super(label, size);
  47     this.previousAverage = -1.0;
  48     this.currentAverage = 0.0;
  49     this.alpha = alpha;
  50   }
  51
  52   @Override
  53   public void updateMostRecentTime(long elapsed) {
  54     if (!enoughStatistics()) {
  55       previousAverage = super.getAverageTime();
  56       super.updateMostRecentTime(elapsed);
  57       if (!enoughStatistics()) {
  58         return;
  59       }
  60     }
  61     // CurrentEMA = α * currentValue + (1 - α) * previousEMA =>
  62     // CurrentEMA = (currentValue - previousEMA) * α + previousEMA
  63     // This will reduce multiplication.
  64     currentAverage = (elapsed - previousAverage) * alpha + previousAverage;
  65     previousAverage = currentAverage;
  66   }
  67
  68   @Override
  69   public double getAverageTime() {
  70     if (!enoughStatistics()) {
  71       return super.getAverageTime();
  72     }
  73     return currentAverage;
  74   }
  75
  76   double getPrevious() {
  77     return previousAverage;
  78   }
  79 }