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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.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;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* A compound stat that reports one or more percentiles
*/
public class Percentiles extends SampledStat implements CompoundStat {
private final Logger log = LoggerFactory.getLogger(Percentiles.class);
public enum BucketSizing {
CONSTANT, LINEAR
}
private final int buckets;
private final Percentile[] percentiles;
private final BinScheme binScheme;
private final double min;
private final double max;
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;
this.min = min;
this.max = max;
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(),
(config, now) -> 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 (Sample s : this.samples) {
HistogramSample sample = (HistogramSample) s;
float[] hist = sample.histogram.counts();
sum += hist[b];
if (sum / count > quant)
return binScheme.fromBin(b);
}
}
return Double.POSITIVE_INFINITY;
}
@Override
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) {
final double boundedValue;
if (value > max) {
log.debug("Received value {} which is greater than max recordable value {}, will be pinned to the max value",
value, max);
boundedValue = max;
} else if (value < min) {
log.debug("Received value {} which is less than min recordable value {}, will be pinned to the min value",
value, min);
boundedValue = min;
} else {
boundedValue = value;
}
HistogramSample hist = (HistogramSample) sample;
hist.histogram.record(boundedValue);
}
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);
}
@Override
public void reset(long now) {
super.reset(now);
this.histogram.clear();
}
}
}
