PostProcessingUtil.java

/**
 * 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
 * <p/>
 * http://www.apache.org/licenses/LICENSE-2.0
 * <p/>
 * 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.hadoop.metrics2.sink.timeline;

import org.apache.commons.math3.analysis.interpolation.LinearInterpolator;
import org.apache.commons.math3.analysis.polynomials.PolynomialFunction;
import org.apache.commons.math3.analysis.polynomials.PolynomialSplineFunction;

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.TreeMap;

public class PostProcessingUtil {

  /*
    Helper function to interpolate missing data on a series.
  */
  public static Map<Long, Double> interpolateMissingData(Map<Long, Double> metricValues, long expectedInterval) {

    if (metricValues == null)
      return null;

    Long prevTime = null;
    Double prevVal = null;
    Map<Long, Double> interpolatedMetricValues = new TreeMap<Long, Double>();

    for (Map.Entry<Long, Double> timeValueEntry : metricValues.entrySet()) {
      Long currTime = timeValueEntry.getKey();
      Double currVal = timeValueEntry.getValue();

      if (prevTime != null) {
        Long stepTime = prevTime;
        while ((currTime - stepTime) > expectedInterval) {
          stepTime+=expectedInterval;
          double interpolatedValue = interpolate(stepTime,
            prevTime, prevVal,
            currTime, currVal);
          interpolatedMetricValues.put(stepTime, interpolatedValue);
        }
      }

      interpolatedMetricValues.put(currTime, currVal);
      prevTime = currTime;
      prevVal = currVal;
    }
    return interpolatedMetricValues;
  }

  public static Double interpolate(Long t, Long t1, Double m1,
                                   Long t2, Double m2) {
    //Linear Interpolation : y = y0 + (y1 - y0) * ((x - x0) / (x1 - x0))
    if (m1 == null && m2 == null) {
      return null;
    }

    if (m1 == null)
      return m2;

    if (m2 == null)
      return m1;

    if (t1 == null || t2 == null)
      return null;

    double slope = (m2 - m1) / (t2 - t1);
    return m1 +  slope * (t - t1);
  }

  public static Map<Long, Double> interpolate(Map<Long, Double> valuesMap, List<Long> requiredTimestamps) {

    LinearInterpolator linearInterpolator = new LinearInterpolator();

    if (valuesMap == null || valuesMap.isEmpty()) {
      return null;
    }
    if (requiredTimestamps == null || requiredTimestamps.isEmpty()) {
      return null;
    }

    Map<Long, Double> interpolatedValuesMap = new HashMap<>();

    if (valuesMap.size() == 1) {
      //Just one value present in the window. Use that value to interpolate all required timestamps.
      Double value  = null;
      for (Map.Entry<Long, Double> entry : valuesMap.entrySet()) {
        value = entry.getValue();
      }
      for (Long requiredTs : requiredTimestamps) {
        interpolatedValuesMap.put(requiredTs, value);
      }
      return interpolatedValuesMap;
    }

    double[] timestamps = new double[valuesMap.size()];
    double[] metrics = new double[valuesMap.size()];

    int i = 0;
    for (Map.Entry<Long, Double> entry : valuesMap.entrySet()) {
      timestamps[i] = (double) entry.getKey();
      metrics[i++] = entry.getValue();
    }

    PolynomialSplineFunction function = linearInterpolator.interpolate(timestamps, metrics);
    PolynomialFunction[] splines = function.getPolynomials();
    PolynomialFunction first = splines[0];

    for (Long requiredTs : requiredTimestamps) {

      Double interpolatedValue = null;
      if (timestampInRange(requiredTs, timestamps[0], timestamps[timestamps.length - 1])) {
        /*
          Interpolation Case
          Required TS is within range of the set of values used for interpolation.
          Hence, we can use library to get the interpolated value.
         */
        interpolatedValue = function.value((double) requiredTs);
      } else {
        /*
        Extrapolation Case
        Required TS outside range of the set of values used for interpolation.
        We will use the coefficients to make best effort extrapolation
        y(x)= y1 + m * (x−x1)
        where, m = (y2−y1)/(x2−x1)
         */
        if (first.getCoefficients() != null && first.getCoefficients().length > 0) {
          /*
          y = c0 + c1x
          where c0, c1 are coefficients
          c1 will not be present if slope is zero.
           */
          Double y1 = first.getCoefficients()[0];
          Double m = (first.getCoefficients().length > 1) ? first.getCoefficients()[1] : 0.0;
          interpolatedValue = y1 + m * (requiredTs - timestamps[0]);
        }
      }

      if (interpolatedValue != null && interpolatedValue >= 0.0) {
        interpolatedValuesMap.put(requiredTs, interpolatedValue);
      }
    }
    return interpolatedValuesMap;
  }

  private static boolean timestampInRange(Long timestamp, double left, double right) {
    return (timestamp >= left && timestamp <= right);
  }

}