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Common library for HPCC functionality
/*******************************************************************************
* HPCC SYSTEMS software Copyright (C) 2020 HPCC Systems®.
*
* Licensed 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.hpccsystems.commons.benchmarking;
import java.util.ArrayList;
import java.lang.Float;
import java.lang.Math;
import org.json.JSONObject;
import org.json.JSONArray;
/**
* A helper class that collects data points for a given metric and provides basic statistical functions.
*/
public class AveragedMetric implements IMetric
{
// First 20 Thompson Tau values. Used to determine if a datapoint is an outlier
// Each datapoint corresponds to a threshold based on the number of samples.
// This table contains only the first 20 values so can only be used for up to 20 samples.
private static final double[] THOMPSON_TAU_TABLE20 = {
0, 0, 1.1511, 1.4250, 1.5712,
1.6563, 1.7110, 1.7491, 1.7770, 1.7984,
1.8153, 1.8290, 1.8403, 1.8498, 1.8579,
1.8649, 1.8710, 1.8764, 1.8811, 1.8853
};
private static final int MAX_DATA_POINTS = 20;
private static final int MIN_DATA_POINTS_FOR_OUTLIERS = 3;
private ArrayList dataPoints = new ArrayList();
private String name = "";
private Units units = null;
private String description = null;
public AveragedMetric(String name)
{
this.name = name;
}
public AveragedMetric(String name, Units units)
{
this.name = name;
this.units = units;
}
public AveragedMetric(String name, Units units, String desc)
{
this.name = name;
this.units = units;
this.description = desc;
}
public AveragedMetric(IMetric metric)
{
this.name = metric.getName();
this.units = metric.getUnits();
this.description = metric.getDescription();
this.addDataPoint(metric.getValue());
}
public void addDataPoint(double dataPoint)
{
if (dataPoints.size() >= MAX_DATA_POINTS)
{
return;
}
dataPoints.add(dataPoint);
}
public void discardOutliers()
{
if (dataPoints.size() < MIN_DATA_POINTS_FOR_OUTLIERS)
{
return;
}
boolean hasOutliers = true;
do
{
// Calculate average of values
double avg = 0;
for (int i = 0; i < dataPoints.size(); i++)
{
avg += dataPoints.get(i).doubleValue();
}
avg /= dataPoints.size();
// Calculate std deviation of values & find largest absolute deviation
double stdDeviation = 0;
double largestAbsDeviation = Float.MIN_VALUE;
int largestAbsDeviationIndex = 0;
for (int i = 0; i < dataPoints.size(); i++)
{
double absoluteDeviation = Math.abs(dataPoints.get(i).doubleValue() - avg);
if (largestAbsDeviation < absoluteDeviation)
{
largestAbsDeviation = absoluteDeviation;
largestAbsDeviationIndex = i;
}
stdDeviation += (absoluteDeviation * absoluteDeviation);
}
stdDeviation /= dataPoints.size();
stdDeviation = (double) Math.sqrt(stdDeviation);
// Check if it can be discarded
double tauDeviation = THOMPSON_TAU_TABLE20[dataPoints.size()-1] * stdDeviation;
if (largestAbsDeviation >= tauDeviation)
{
hasOutliers = true;
dataPoints.remove(largestAbsDeviationIndex);
}
else
{
hasOutliers = false;
}
// If we discarded an outlier repeat above steps
} while (hasOutliers && dataPoints.size() > 2);
}
public double getMin()
{
if (dataPoints.size() == 0)
{
return 0;
}
double smallestValue = dataPoints.get(0).doubleValue();
for (int i = 1; i < dataPoints.size(); i++)
{
if (dataPoints.get(i).doubleValue() < smallestValue)
{
smallestValue = dataPoints.get(i).doubleValue();
}
}
return smallestValue;
}
public double getMax()
{
if (dataPoints.size() == 0)
{
return 0;
}
double largestValue = dataPoints.get(0).doubleValue();
for (int i = 1; i < dataPoints.size(); i++)
{
if (dataPoints.get(i).doubleValue() > largestValue)
{
largestValue = dataPoints.get(i).doubleValue();
}
}
return largestValue;
}
public double getAvg()
{
if (dataPoints.size() == 0)
{
return 0;
}
double avgValue = 0;
for (int i = 0; i < dataPoints.size(); i++)
{
avgValue += dataPoints.get(i).doubleValue();
}
avgValue /= dataPoints.size();
return avgValue;
}
public double getStdDev()
{
double avg = getAvg();
double stdDeviation = 0;
for (int i = 0; i < dataPoints.size(); i++)
{
double absoluteDeviation = dataPoints.get(i).doubleValue() - avg;
stdDeviation += (absoluteDeviation * absoluteDeviation);
}
stdDeviation /= dataPoints.size();
stdDeviation = (double) Math.sqrt(stdDeviation);
return stdDeviation;
}
public void toJson(JSONArray metricArray)
{
discardOutliers();
// Min
JSONObject obj = new JSONObject();
obj.put("name", name + ".min");
if (units != null)
{
obj.put("units",units);
}
if (description != null)
{
obj.put("description",description);
}
obj.put("value",getMin());
metricArray.put(obj);
// Max
obj = new JSONObject();
obj.put("name", name + ".max");
if (units != null)
{
obj.put("units",units);
}
if (description != null)
{
obj.put("description",description);
}
obj.put("value",getMax());
metricArray.put(obj);
// Avg
obj = new JSONObject();
obj.put("name", name + ".avg");
if (units != null)
{
obj.put("units",units);
}
if (description != null)
{
obj.put("description",description);
}
obj.put("value",getAvg());
metricArray.put(obj);
// Std deviation
obj = new JSONObject();
obj.put("name", name + ".std_dev");
if (units != null)
{
obj.put("units",units);
}
if (description != null)
{
obj.put("description",description);
}
obj.put("value",getStdDev());
metricArray.put(obj);
}
public double getValue()
{
discardOutliers();
return getAvg();
}
public String getName()
{
return name;
}
public String getDescription()
{
return description;
}
public Units getUnits()
{
return units;
}
};
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