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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.hadoop.metrics2.lib;
import static org.apache.hadoop.metrics2.lib.Interns.info;
import java.util.Map;
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.ScheduledFuture;
import java.util.concurrent.TimeUnit;
import io.prestosql.hadoop.$internal.org.apache.commons.lang3.StringUtils;
import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.metrics2.MetricsInfo;
import org.apache.hadoop.metrics2.MetricsRecordBuilder;
import org.apache.hadoop.metrics2.util.Quantile;
import org.apache.hadoop.metrics2.util.QuantileEstimator;
import org.apache.hadoop.metrics2.util.SampleQuantiles;
import io.prestosql.hadoop.$internal.com.google.common.annotations.VisibleForTesting;
import io.prestosql.hadoop.$internal.com.google.common.util.concurrent.ThreadFactoryBuilder;
/**
* Watches a stream of long values, maintaining online estimates of specific
* quantiles with provably low error bounds. This is particularly useful for
* accurate high-percentile (e.g. 95th, 99th) latency metrics.
*/
@InterfaceAudience.Public
@InterfaceStability.Evolving
public class MutableQuantiles extends MutableMetric {
@VisibleForTesting
public static final Quantile[] quantiles = { new Quantile(0.50, 0.050),
new Quantile(0.75, 0.025), new Quantile(0.90, 0.010),
new Quantile(0.95, 0.005), new Quantile(0.99, 0.001) };
private final MetricsInfo numInfo;
private final MetricsInfo[] quantileInfos;
private final int interval;
private QuantileEstimator estimator;
private long previousCount = 0;
private ScheduledFuture> scheduledTask = null;
@VisibleForTesting
protected Map previousSnapshot = null;
private static final ScheduledExecutorService scheduler = Executors
.newScheduledThreadPool(1, new ThreadFactoryBuilder().setDaemon(true)
.setNameFormat("MutableQuantiles-%d").build());
/**
* Instantiates a new {@link MutableQuantiles} for a metric that rolls itself
* over on the specified time interval.
*
* @param name
* of the metric
* @param description
* long-form textual description of the metric
* @param sampleName
* type of items in the stream (e.g., "Ops")
* @param valueName
* type of the values
* @param interval
* rollover interval (in seconds) of the estimator
*/
public MutableQuantiles(String name, String description, String sampleName,
String valueName, int interval) {
String ucName = StringUtils.capitalize(name);
String usName = StringUtils.capitalize(sampleName);
String uvName = StringUtils.capitalize(valueName);
String desc = StringUtils.uncapitalize(description);
String lsName = StringUtils.uncapitalize(sampleName);
String lvName = StringUtils.uncapitalize(valueName);
numInfo = info(ucName + "Num" + usName, String.format(
"Number of %s for %s with %ds interval", lsName, desc, interval));
// Construct the MetricsInfos for the quantiles, converting to percentiles
quantileInfos = new MetricsInfo[quantiles.length];
String nameTemplate = ucName + "%dthPercentile" + uvName;
String descTemplate = "%d percentile " + lvName + " with " + interval
+ " second interval for " + desc;
for (int i = 0; i < quantiles.length; i++) {
int percentile = (int) (100 * quantiles[i].quantile);
quantileInfos[i] = info(String.format(nameTemplate, percentile),
String.format(descTemplate, percentile));
}
estimator = new SampleQuantiles(quantiles);
this.interval = interval;
scheduledTask = scheduler.scheduleAtFixedRate(new RolloverSample(this),
interval, interval, TimeUnit.SECONDS);
}
@Override
public synchronized void snapshot(MetricsRecordBuilder builder, boolean all) {
if (all || changed()) {
builder.addGauge(numInfo, previousCount);
for (int i = 0; i < quantiles.length; i++) {
long newValue = 0;
// If snapshot is null, we failed to update since the window was empty
if (previousSnapshot != null) {
newValue = previousSnapshot.get(quantiles[i]);
}
builder.addGauge(quantileInfos[i], newValue);
}
if (changed()) {
clearChanged();
}
}
}
public synchronized void add(long value) {
estimator.insert(value);
}
public int getInterval() {
return interval;
}
public void stop() {
if (scheduledTask != null) {
scheduledTask.cancel(false);
}
scheduledTask = null;
}
/**
* Get the quantile estimator.
*
* @return the quantile estimator
*/
@VisibleForTesting
public synchronized QuantileEstimator getEstimator() {
return estimator;
}
public synchronized void setEstimator(QuantileEstimator quantileEstimator) {
this.estimator = quantileEstimator;
}
/**
* Runnable used to periodically roll over the internal
* {@link SampleQuantiles} every interval.
*/
private static class RolloverSample implements Runnable {
MutableQuantiles parent;
public RolloverSample(MutableQuantiles parent) {
this.parent = parent;
}
@Override
public void run() {
synchronized (parent) {
parent.previousCount = parent.estimator.getCount();
parent.previousSnapshot = parent.estimator.snapshot();
parent.estimator.clear();
}
parent.setChanged();
}
}
}