com.netflix.servo.stats.StatsBuffer Maven / Gradle / Ivy
/*
* Copyright 2014 Netflix, Inc.
*
* 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 com.netflix.servo.stats;
import com.netflix.servo.util.Preconditions;
import java.util.Arrays;
import java.util.concurrent.atomic.AtomicBoolean;
/**
* A simple circular buffer that records values, and computes useful stats.
* This implementation is not thread safe.
*/
public class StatsBuffer {
private int count;
private double mean;
private double sumSquares;
private double variance;
private double stddev;
private long min;
private long max;
private long total;
private double[] percentiles;
private double[] percentileValues;
private final int size;
private final long[] values;
private AtomicBoolean statsComputed = new AtomicBoolean(false);
/**
* Create a circular buffer that will be used to record values and compute useful stats.
*
* @param size The capacity of the buffer
* @param percentiles Array of percentiles to compute. For example { 95.0, 99.0 }.
* If no percentileValues are required pass a 0-sized array.
*/
public StatsBuffer(int size, double[] percentiles) {
Preconditions.checkArgument(size > 0, "Size of the buffer must be greater than 0");
Preconditions.checkArgument(percentiles != null,
"Percents array must be non-null. Pass a 0-sized array "
+ "if you don't want any percentileValues to be computed.");
Preconditions.checkArgument(validPercentiles(percentiles),
"All percentiles should be in the interval (0.0, 100.0]");
values = new long[size];
this.size = size;
this.percentiles = Arrays.copyOf(percentiles, percentiles.length);
this.percentileValues = new double[percentiles.length];
reset();
}
private static boolean validPercentiles(double[] percentiles) {
for (double percentile : percentiles) {
if (percentile <= 0.0 || percentile > 100.0) {
return false;
}
}
return true;
}
/**
* Reset our local state: All values are set to 0.
*/
public void reset() {
statsComputed.set(false);
count = 0;
total = 0L;
mean = 0.0;
variance = 0.0;
stddev = 0.0;
min = 0L;
max = 0L;
sumSquares = 0.0;
for (int i = 0; i < percentileValues.length; ++i) {
percentileValues[i] = 0.0;
}
}
/**
* Record a new value for this buffer.
*/
public void record(long n) {
values[count++ % size] = n;
total += n;
sumSquares += n * n;
}
/**
* Compute stats for the current set of values.
*/
public void computeStats() {
if (statsComputed.getAndSet(true)) {
return;
}
if (count == 0) {
return;
}
int curSize = Math.min(count, size);
Arrays.sort(values, 0, curSize); // to compute percentileValues
min = values[0];
max = values[curSize - 1];
mean = (double) total / count;
variance = (sumSquares / curSize) - (mean * mean);
stddev = Math.sqrt(variance);
computePercentiles(curSize);
}
private void computePercentiles(int curSize) {
for (int i = 0; i < percentiles.length; ++i) {
percentileValues[i] = calcPercentile(curSize, percentiles[i]);
}
}
private double calcPercentile(int curSize, double percent) {
if (curSize == 0) {
return 0.0;
}
if (curSize == 1) {
return values[0];
}
/*
* We use the definition from http://cnx.org/content/m10805/latest
* modified for 0-indexed arrays.
*/
final double rank = percent * curSize / 100.0; // SUPPRESS CHECKSTYLE MagicNumber
final int ir = (int) Math.floor(rank);
final int irNext = ir + 1;
final double fr = rank - ir;
if (irNext >= curSize) {
return values[curSize - 1];
} else if (fr == 0.0) {
return values[ir];
} else {
// Interpolate between the two bounding values
final double lower = values[ir];
final double upper = values[irNext];
return fr * (upper - lower) + lower;
}
}
/**
* Get the number of entries recorded.
*/
public int getCount() {
return count;
}
/**
* Get the average of the values recorded.
*
* @return The average of the values recorded, or 0.0 if no values were recorded.
*/
public double getMean() {
return mean;
}
/**
* Get the variance for the population of the recorded values present in our buffer.
*
* @return The variance.p of the values recorded, or 0.0 if no values were recorded.
*/
public double getVariance() {
return variance;
}
/**
* Get the standard deviation for the population of the recorded values present in our buffer.
*
* @return The stddev.p of the values recorded, or 0.0 if no values were recorded.
*/
public double getStdDev() {
return stddev;
}
/**
* Get the minimum of the values currently in our buffer.
*
* @return The min of the values recorded, or 0.0 if no values were recorded.
*/
public long getMin() {
return min;
}
/**
* Get the max of the values currently in our buffer.
*
* @return The max of the values recorded, or 0.0 if no values were recorded.
*/
public long getMax() {
return max;
}
/**
* Get the total sum of the values recorded.
*
* @return The sum of the values recorded, or 0.0 if no values were recorded.
*/
public long getTotalTime() {
return total;
}
/**
* Get the computed percentileValues. See {@link StatsConfig} for how to request different
* percentileValues. Note that for efficiency reasons we return the actual array of
* computed values.
* Users must NOT modify this array.
*
* @return An array of computed percentileValues.
*/
@edu.umd.cs.findbugs.annotations.SuppressWarnings(value = "EI_EXPOSE_REP",
justification = "Performance critical code. Users treat it as read-only")
public double[] getPercentileValues() {
return percentileValues;
}
/**
* Return the percentiles we will compute: For example: 95.0, 99.0.
*/
public double[] getPercentiles() {
return Arrays.copyOf(percentiles, percentiles.length);
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy