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/* =========================================================================================
* Copyright © 2013-2017 the kamon project <http://kamon.io/>
*
* 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 kamon
package metric
import java.nio.ByteBuffer
import kamon.util.MeasurementUnit
import org.HdrHistogram.{AtomicHistogramExtension, ZigZag}
import org.slf4j.LoggerFactory
trait Histogram {
def unit: MeasurementUnit
def dynamicRange: DynamicRange
def record(value: Long): Unit
def record(value: Long, times: Long): Unit
}
private[kamon] class HdrHistogram(name: String, tags: Map[String, String], val unit: MeasurementUnit, val dynamicRange: DynamicRange)
extends AtomicHistogramExtension(dynamicRange) with Histogram {
def record(value: Long): Unit =
tryRecord(value, 1)
def record(value: Long, count: Long): Unit =
tryRecord(value, count)
private def tryRecord(value: Long, count: Long): Unit = {
try {
recordValueWithCount(value, count)
} catch {
case anyException: Throwable ⇒
HdrHistogram.logger.warn(s"Failed to store value [$value] in histogram [$name]. You might need to change " +
"your dynamic range configuration for this instrument.", anyException)
}
}
def snapshot(): MetricDistribution = {
val buffer = HdrHistogram.tempSnapshotBuffer.get()
val counts = countsArray()
val countsLimit = counts.length()
var index = 0
buffer.clear()
var minIndex = Int.MaxValue
var maxIndex = 0
var totalCount = 0L
while(index < countsLimit) {
val countAtIndex = counts.getAndSet(index, 0L)
var zerosCount = 0L
if(countAtIndex == 0L) {
index += 1
zerosCount = 1
while(index < countsLimit && counts.get(index) == 0L) {
index += 1
zerosCount += 1
}
}
if(zerosCount > 0) {
if(index < countsLimit)
ZigZag.putLong(buffer, -zerosCount)
}
else {
if(minIndex > index)
minIndex = index
maxIndex = index
index += 1
totalCount += countAtIndex
ZigZag.putLong(buffer, countAtIndex)
}
}
buffer.flip()
val zigZagCounts = Array.ofDim[Byte](buffer.limit())
buffer.get(zigZagCounts)
val distribution = new ZigZagCountsDistribution(totalCount, minIndex, maxIndex, ByteBuffer.wrap(zigZagCounts),
protectedUnitMagnitude(), protectedSubBucketHalfCount(), protectedSubBucketHalfCountMagnitude())
MetricDistribution(name, tags, unit, dynamicRange, distribution)
}
private class ZigZagCountsDistribution(val count: Long, minIndex: Int, maxIndex: Int, zigZagCounts: ByteBuffer,
unitMagnitude: Int, subBucketHalfCount: Int, subBucketHalfCountMagnitude: Int) extends Distribution {
val min: Long = if(count == 0) 0 else bucketValueAtIndex(minIndex)
val max: Long = bucketValueAtIndex(maxIndex)
def sum: Long = bucketsIterator.foldLeft(0L)((a, b) => a + (b.value * b.frequency))
def buckets: Seq[Bucket] = {
val builder = Vector.newBuilder[Bucket]
bucketsIterator.foreach { b =>
builder += DefaultBucket(b.value, b.frequency)
}
builder.result()
}
def bucketsIterator: Iterator[Bucket] = new Iterator[Bucket] {
val buffer = zigZagCounts.duplicate()
val bucket = MutableBucket(0, 0)
var countsArrayIndex = 0
def hasNext: Boolean =
buffer.remaining() > 0
def next(): Bucket = {
val readLong = ZigZag.getLong(buffer)
val frequency = if(readLong > 0) {
readLong
} else {
countsArrayIndex += (-readLong.toInt)
ZigZag.getLong(buffer)
}
bucket.value = bucketValueAtIndex(countsArrayIndex)
bucket.frequency = frequency
countsArrayIndex += 1
bucket
}
}
def percentilesIterator: Iterator[Percentile] = new Iterator[Percentile]{
val buckets = bucketsIterator
val percentile = MutablePercentile(0D, 0, 0)
var countUnderQuantile = 0L
def hasNext: Boolean =
buckets.hasNext
def next(): Percentile = {
val bucket = buckets.next()
countUnderQuantile += bucket.frequency
percentile.quantile = (countUnderQuantile * 100D) / ZigZagCountsDistribution.this.count
percentile.countUnderQuantile = countUnderQuantile
percentile.value = bucket.value
percentile
}
}
def percentile(p: Double): Percentile = {
val percentiles = percentilesIterator
if(percentiles.hasNext) {
var currentPercentile = percentiles.next()
while(percentiles.hasNext && currentPercentile.quantile < p) {
currentPercentile = percentiles.next()
}
currentPercentile
} else DefaultPercentile(p, 0, 0)
}
def percentiles: Seq[Percentile] = {
val builder = Vector.newBuilder[Percentile]
percentilesIterator.foreach { p =>
builder += DefaultPercentile(p.quantile, p.value, p.countUnderQuantile)
}
builder.result()
}
@inline private def bucketValueAtIndex(index: Int): Long = {
var bucketIndex: Int = (index >> subBucketHalfCountMagnitude) - 1
var subBucketIndex: Int = (index & (subBucketHalfCount - 1)) + subBucketHalfCount
if (bucketIndex < 0) {
subBucketIndex -= subBucketHalfCount
bucketIndex = 0
}
subBucketIndex.toLong << (bucketIndex + unitMagnitude)
}
}
case class DefaultBucket(value: Long, frequency: Long) extends Bucket
case class MutableBucket(var value: Long, var frequency: Long) extends Bucket
case class DefaultPercentile(quantile: Double, value: Long, countUnderQuantile: Long) extends Percentile
case class MutablePercentile(var quantile: Double, var value: Long, var countUnderQuantile: Long) extends Percentile
}
object HdrHistogram {
private val logger = LoggerFactory.getLogger(classOf[HdrHistogram])
// TODO: move this to some object pool might be better, or at
private val tempSnapshotBuffer = new ThreadLocal[ByteBuffer] {
override def initialValue(): ByteBuffer = ByteBuffer.allocate(33792)
}
}
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