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Metrics is a Java library which gives you unparalleled insight into what your code does in
production. Metrics provides a powerful toolkit of ways to measure the behavior of critical
components in your production environment.
package com.codahale.metrics;
import java.io.OutputStream;
import java.io.OutputStreamWriter;
import java.io.PrintWriter;
import java.nio.charset.Charset;
import java.util.Arrays;
import java.util.Collection;
import static java.lang.Math.floor;
/**
* A statistical snapshot of a {@link Snapshot}.
*/
public class Snapshot {
private static final Charset UTF_8 = Charset.forName("UTF-8");
private final long[] values;
/**
* Create a new {@link Snapshot} with the given values.
*
* @param values an unordered set of values in the reservoir
*/
public Snapshot(Collection values) {
final Object[] copy = values.toArray();
this.values = new long[copy.length];
for (int i = 0; i < copy.length; i++) {
this.values[i] = (Long) copy[i];
}
Arrays.sort(this.values);
}
/**
* Create a new {@link Snapshot} with the given values.
*
* @param values an unordered set of values in the reservoir
*/
public Snapshot(long[] values) {
this.values = Arrays.copyOf(values, values.length);
Arrays.sort(this.values);
}
/**
* Returns the value at the given quantile.
*
* @param quantile a given quantile, in {@code [0..1]}
* @return the value in the distribution at {@code quantile}
*/
public double getValue(double quantile) {
if (quantile < 0.0 || quantile > 1.0) {
throw new IllegalArgumentException(quantile + " is not in [0..1]");
}
if (values.length == 0) {
return 0.0;
}
final double pos = quantile * (values.length + 1);
if (pos < 1) {
return values[0];
}
if (pos >= values.length) {
return values[values.length - 1];
}
final double lower = values[(int) pos - 1];
final double upper = values[(int) pos];
return lower + (pos - floor(pos)) * (upper - lower);
}
/**
* Returns the number of values in the snapshot.
*
* @return the number of values
*/
public int size() {
return values.length;
}
/**
* Returns the median value in the distribution.
*
* @return the median value
*/
public double getMedian() {
return getValue(0.5);
}
/**
* Returns the value at the 75th percentile in the distribution.
*
* @return the value at the 75th percentile
*/
public double get75thPercentile() {
return getValue(0.75);
}
/**
* Returns the value at the 95th percentile in the distribution.
*
* @return the value at the 95th percentile
*/
public double get95thPercentile() {
return getValue(0.95);
}
/**
* Returns the value at the 98th percentile in the distribution.
*
* @return the value at the 98th percentile
*/
public double get98thPercentile() {
return getValue(0.98);
}
/**
* Returns the value at the 99th percentile in the distribution.
*
* @return the value at the 99th percentile
*/
public double get99thPercentile() {
return getValue(0.99);
}
/**
* Returns the value at the 99.9th percentile in the distribution.
*
* @return the value at the 99.9th percentile
*/
public double get999thPercentile() {
return getValue(0.999);
}
/**
* Returns the entire set of values in the snapshot.
*
* @return the entire set of values
*/
public long[] getValues() {
return Arrays.copyOf(values, values.length);
}
/**
* Returns the highest value in the snapshot.
*
* @return the highest value
*/
public long getMax() {
if (values.length == 0) {
return 0;
}
return values[values.length - 1];
}
/**
* Returns the lowest value in the snapshot.
*
* @return the lowest value
*/
public long getMin() {
if (values.length == 0) {
return 0;
}
return values[0];
}
/**
* Returns the arithmetic mean of the values in the snapshot.
*
* @return the arithmetic mean
*/
public double getMean() {
if (values.length == 0) {
return 0;
}
double sum = 0;
for (long value : values) {
sum += value;
}
return sum / values.length;
}
/**
* Returns the standard deviation of the values in the snapshot.
*
* @return the standard value
*/
public double getStdDev() {
// two-pass algorithm for variance, avoids numeric overflow
if (values.length <= 1) {
return 0;
}
final double mean = getMean();
double sum = 0;
for (long value : values) {
final double diff = value - mean;
sum += diff * diff;
}
final double variance = sum / (values.length - 1);
return Math.sqrt(variance);
}
/**
* Writes the values of the snapshot to the given stream.
*
* @param output an output stream
*/
public void dump(OutputStream output) {
final PrintWriter out = new PrintWriter(new OutputStreamWriter(output, UTF_8));
try {
for (long value : values) {
out.printf("%d%n", value);
}
} finally {
out.close();
}
}
}