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/**
* 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
*
* 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.apache.kafka.common.metrics.stats;
import java.util.ArrayList;
import java.util.List;
import org.apache.kafka.common.metrics.CompoundStat;
import org.apache.kafka.common.metrics.Measurable;
import org.apache.kafka.common.metrics.MetricConfig;
import org.apache.kafka.common.metrics.stats.Histogram.BinScheme;
import org.apache.kafka.common.metrics.stats.Histogram.ConstantBinScheme;
import org.apache.kafka.common.metrics.stats.Histogram.LinearBinScheme;
/**
* A compound stat that reports one or more percentiles
*/
public class Percentiles extends SampledStat implements CompoundStat {
public static enum BucketSizing {
CONSTANT, LINEAR
}
private final int buckets;
private final Percentile[] percentiles;
private final BinScheme binScheme;
public Percentiles(int sizeInBytes, double max, BucketSizing bucketing, Percentile... percentiles) {
this(sizeInBytes, 0.0, max, bucketing, percentiles);
}
public Percentiles(int sizeInBytes, double min, double max, BucketSizing bucketing, Percentile... percentiles) {
super(0.0);
this.percentiles = percentiles;
this.buckets = sizeInBytes / 4;
if (bucketing == BucketSizing.CONSTANT) {
this.binScheme = new ConstantBinScheme(buckets, min, max);
} else if (bucketing == BucketSizing.LINEAR) {
if (min != 0.0d)
throw new IllegalArgumentException("Linear bucket sizing requires min to be 0.0.");
this.binScheme = new LinearBinScheme(buckets, max);
} else {
throw new IllegalArgumentException("Unknown bucket type: " + bucketing);
}
}
@Override
public List stats() {
List ms = new ArrayList(this.percentiles.length);
for (Percentile percentile : this.percentiles) {
final double pct = percentile.percentile();
ms.add(new NamedMeasurable(percentile.name(), new Measurable() {
public double measure(MetricConfig config, long now) {
return value(config, now, pct / 100.0);
}
}));
}
return ms;
}
public double value(MetricConfig config, long now, double quantile) {
purgeObsoleteSamples(config, now);
float count = 0.0f;
for (Sample sample : this.samples)
count += sample.eventCount;
if (count == 0.0f)
return Double.NaN;
float sum = 0.0f;
float quant = (float) quantile;
for (int b = 0; b < buckets; b++) {
for (int s = 0; s < this.samples.size(); s++) {
HistogramSample sample = (HistogramSample) this.samples.get(s);
float[] hist = sample.histogram.counts();
sum += hist[b];
if (sum / count > quant)
return binScheme.fromBin(b);
}
}
return Double.POSITIVE_INFINITY;
}
public double combine(List samples, MetricConfig config, long now) {
return value(config, now, 0.5);
}
@Override
protected HistogramSample newSample(long timeMs) {
return new HistogramSample(this.binScheme, timeMs);
}
@Override
protected void update(Sample sample, MetricConfig config, double value, long timeMs) {
HistogramSample hist = (HistogramSample) sample;
hist.histogram.record(value);
}
private static class HistogramSample extends SampledStat.Sample {
private final Histogram histogram;
private HistogramSample(BinScheme scheme, long now) {
super(0.0, now);
this.histogram = new Histogram(scheme);
}
}
}
