io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.scala Maven / Gradle / Ivy
// Generated by the Scala Plugin for the Protocol Buffer Compiler.
// Do not edit!
//
// Protofile syntax: PROTO3
package io.opentelemetry.proto.metrics.v1
/** ExponentialHistogramDataPoint is a single data point in a timeseries that describes the
* time-varying values of a ExponentialHistogram of double values. A ExponentialHistogram contains
* summary statistics for a population of values, it may optionally contain the
* distribution of those values across a set of buckets.
*
* @param attributes
* The set of key/value pairs that uniquely identify the timeseries from
* where this point belongs. The list may be empty (may contain 0 elements).
* Attribute keys MUST be unique (it is not allowed to have more than one
* attribute with the same key).
* @param startTimeUnixNano
* StartTimeUnixNano is optional but strongly encouraged, see the
* the detailed comments above Metric.
*
* Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
* 1970.
* @param timeUnixNano
* TimeUnixNano is required, see the detailed comments above Metric.
*
* Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
* 1970.
* @param count
* count is the number of values in the population. Must be
* non-negative. This value must be equal to the sum of the "bucket_counts"
* values in the positive and negative Buckets plus the "zero_count" field.
* @param sum
* sum of the values in the population. If count is zero then this field
* must be zero.
*
* Note: Sum should only be filled out when measuring non-negative discrete
* events, and is assumed to be monotonic over the values of these events.
* Negative events *can* be recorded, but sum should not be filled out when
* doing so. This is specifically to enforce compatibility w/ OpenMetrics,
* see: https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md#histogram
* @param scale
* scale describes the resolution of the histogram. Boundaries are
* located at powers of the base, where:
*
* base = (2^(2^-scale))
*
* The histogram bucket identified by `index`, a signed integer,
* contains values that are greater than or equal to (base^index) and
* less than (base^(index+1)).
*
* The positive and negative ranges of the histogram are expressed
* separately. Negative values are mapped by their absolute value
* into the negative range using the same scale as the positive range.
*
* scale is not restricted by the protocol, as the permissible
* values depend on the range of the data.
* @param zeroCount
* zero_count is the count of values that are either exactly zero or
* within the region considered zero by the instrumentation at the
* tolerated degree of precision. This bucket stores values that
* cannot be expressed using the standard exponential formula as
* well as values that have been rounded to zero.
*
* Implementations MAY consider the zero bucket to have probability
* mass equal to (zero_count / count).
* @param positive
* positive carries the positive range of exponential bucket counts.
* @param negative
* negative carries the negative range of exponential bucket counts.
* @param flags
* Flags that apply to this specific data point. See DataPointFlags
* for the available flags and their meaning.
* @param exemplars
* (Optional) List of exemplars collected from
* measurements that were used to form the data point
* @param min
* min is the minimum value over (start_time, end_time].
* @param max
* max is the maximum value over (start_time, end_time].
*/
@SerialVersionUID(0L)
final case class ExponentialHistogramDataPoint(
attributes: _root_.scala.Seq[io.opentelemetry.proto.common.v1.KeyValue] = _root_.scala.Seq.empty,
startTimeUnixNano: _root_.scala.Long = 0L,
timeUnixNano: _root_.scala.Long = 0L,
count: _root_.scala.Long = 0L,
sum: _root_.scala.Option[_root_.scala.Double] = _root_.scala.None,
scale: _root_.scala.Int = 0,
zeroCount: _root_.scala.Long = 0L,
positive: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] = _root_.scala.None,
negative: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] = _root_.scala.None,
flags: _root_.scala.Int = 0,
exemplars: _root_.scala.Seq[io.opentelemetry.proto.metrics.v1.Exemplar] = _root_.scala.Seq.empty,
min: _root_.scala.Option[_root_.scala.Double] = _root_.scala.None,
max: _root_.scala.Option[_root_.scala.Double] = _root_.scala.None,
unknownFields: _root_.scalapb.UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty
) extends scalapb.GeneratedMessage with scalapb.lenses.Updatable[ExponentialHistogramDataPoint] {
@transient
private[this] var __serializedSizeMemoized: _root_.scala.Int = 0
private[this] def __computeSerializedSize(): _root_.scala.Int = {
var __size = 0
attributes.foreach { __item =>
val __value = __item
__size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
}
{
val __value = startTimeUnixNano
if (__value != 0L) {
__size += _root_.com.google.protobuf.CodedOutputStream.computeFixed64Size(2, __value)
}
};
{
val __value = timeUnixNano
if (__value != 0L) {
__size += _root_.com.google.protobuf.CodedOutputStream.computeFixed64Size(3, __value)
}
};
{
val __value = count
if (__value != 0L) {
__size += _root_.com.google.protobuf.CodedOutputStream.computeFixed64Size(4, __value)
}
};
if (sum.isDefined) {
val __value = sum.get
__size += _root_.com.google.protobuf.CodedOutputStream.computeDoubleSize(5, __value)
};
{
val __value = scale
if (__value != 0) {
__size += _root_.com.google.protobuf.CodedOutputStream.computeSInt32Size(6, __value)
}
};
{
val __value = zeroCount
if (__value != 0L) {
__size += _root_.com.google.protobuf.CodedOutputStream.computeFixed64Size(7, __value)
}
};
if (positive.isDefined) {
val __value = positive.get
__size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
};
if (negative.isDefined) {
val __value = negative.get
__size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
};
{
val __value = flags
if (__value != 0) {
__size += _root_.com.google.protobuf.CodedOutputStream.computeUInt32Size(10, __value)
}
};
exemplars.foreach { __item =>
val __value = __item
__size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
}
if (min.isDefined) {
val __value = min.get
__size += _root_.com.google.protobuf.CodedOutputStream.computeDoubleSize(12, __value)
};
if (max.isDefined) {
val __value = max.get
__size += _root_.com.google.protobuf.CodedOutputStream.computeDoubleSize(13, __value)
};
__size += unknownFields.serializedSize
__size
}
override def serializedSize: _root_.scala.Int = {
var __size = __serializedSizeMemoized
if (__size == 0) {
__size = __computeSerializedSize() + 1
__serializedSizeMemoized = __size
}
__size - 1
}
def writeTo(`_output__`: _root_.com.google.protobuf.CodedOutputStream): _root_.scala.Unit = {
attributes.foreach { __v =>
val __m = __v
_output__.writeTag(1, 2)
_output__.writeUInt32NoTag(__m.serializedSize)
__m.writeTo(_output__)
};
{
val __v = startTimeUnixNano
if (__v != 0L) {
_output__.writeFixed64(2, __v)
}
};
{
val __v = timeUnixNano
if (__v != 0L) {
_output__.writeFixed64(3, __v)
}
};
{
val __v = count
if (__v != 0L) {
_output__.writeFixed64(4, __v)
}
};
sum.foreach { __v =>
val __m = __v
_output__.writeDouble(5, __m)
};
{
val __v = scale
if (__v != 0) {
_output__.writeSInt32(6, __v)
}
};
{
val __v = zeroCount
if (__v != 0L) {
_output__.writeFixed64(7, __v)
}
};
positive.foreach { __v =>
val __m = __v
_output__.writeTag(8, 2)
_output__.writeUInt32NoTag(__m.serializedSize)
__m.writeTo(_output__)
};
negative.foreach { __v =>
val __m = __v
_output__.writeTag(9, 2)
_output__.writeUInt32NoTag(__m.serializedSize)
__m.writeTo(_output__)
};
{
val __v = flags
if (__v != 0) {
_output__.writeUInt32(10, __v)
}
};
exemplars.foreach { __v =>
val __m = __v
_output__.writeTag(11, 2)
_output__.writeUInt32NoTag(__m.serializedSize)
__m.writeTo(_output__)
};
min.foreach { __v =>
val __m = __v
_output__.writeDouble(12, __m)
};
max.foreach { __v =>
val __m = __v
_output__.writeDouble(13, __m)
};
unknownFields.writeTo(_output__)
}
def clearAttributes = copy(attributes = _root_.scala.Seq.empty)
def addAttributes(__vs: io.opentelemetry.proto.common.v1.KeyValue *): ExponentialHistogramDataPoint = addAllAttributes(__vs)
def addAllAttributes(__vs: Iterable[io.opentelemetry.proto.common.v1.KeyValue]): ExponentialHistogramDataPoint = copy(attributes = attributes ++ __vs)
def withAttributes(__v: _root_.scala.Seq[io.opentelemetry.proto.common.v1.KeyValue]): ExponentialHistogramDataPoint = copy(attributes = __v)
def withStartTimeUnixNano(__v: _root_.scala.Long): ExponentialHistogramDataPoint = copy(startTimeUnixNano = __v)
def withTimeUnixNano(__v: _root_.scala.Long): ExponentialHistogramDataPoint = copy(timeUnixNano = __v)
def withCount(__v: _root_.scala.Long): ExponentialHistogramDataPoint = copy(count = __v)
def getSum: _root_.scala.Double = sum.getOrElse(0.0)
def clearSum: ExponentialHistogramDataPoint = copy(sum = _root_.scala.None)
def withSum(__v: _root_.scala.Double): ExponentialHistogramDataPoint = copy(sum = Option(__v))
def withScale(__v: _root_.scala.Int): ExponentialHistogramDataPoint = copy(scale = __v)
def withZeroCount(__v: _root_.scala.Long): ExponentialHistogramDataPoint = copy(zeroCount = __v)
def getPositive: io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets = positive.getOrElse(io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets.defaultInstance)
def clearPositive: ExponentialHistogramDataPoint = copy(positive = _root_.scala.None)
def withPositive(__v: io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets): ExponentialHistogramDataPoint = copy(positive = Option(__v))
def getNegative: io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets = negative.getOrElse(io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets.defaultInstance)
def clearNegative: ExponentialHistogramDataPoint = copy(negative = _root_.scala.None)
def withNegative(__v: io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets): ExponentialHistogramDataPoint = copy(negative = Option(__v))
def withFlags(__v: _root_.scala.Int): ExponentialHistogramDataPoint = copy(flags = __v)
def clearExemplars = copy(exemplars = _root_.scala.Seq.empty)
def addExemplars(__vs: io.opentelemetry.proto.metrics.v1.Exemplar *): ExponentialHistogramDataPoint = addAllExemplars(__vs)
def addAllExemplars(__vs: Iterable[io.opentelemetry.proto.metrics.v1.Exemplar]): ExponentialHistogramDataPoint = copy(exemplars = exemplars ++ __vs)
def withExemplars(__v: _root_.scala.Seq[io.opentelemetry.proto.metrics.v1.Exemplar]): ExponentialHistogramDataPoint = copy(exemplars = __v)
def getMin: _root_.scala.Double = min.getOrElse(0.0)
def clearMin: ExponentialHistogramDataPoint = copy(min = _root_.scala.None)
def withMin(__v: _root_.scala.Double): ExponentialHistogramDataPoint = copy(min = Option(__v))
def getMax: _root_.scala.Double = max.getOrElse(0.0)
def clearMax: ExponentialHistogramDataPoint = copy(max = _root_.scala.None)
def withMax(__v: _root_.scala.Double): ExponentialHistogramDataPoint = copy(max = Option(__v))
def withUnknownFields(__v: _root_.scalapb.UnknownFieldSet) = copy(unknownFields = __v)
def discardUnknownFields = copy(unknownFields = _root_.scalapb.UnknownFieldSet.empty)
def getFieldByNumber(__fieldNumber: _root_.scala.Int): _root_.scala.Any = {
(__fieldNumber: @_root_.scala.unchecked) match {
case 1 => attributes
case 2 => {
val __t = startTimeUnixNano
if (__t != 0L) __t else null
}
case 3 => {
val __t = timeUnixNano
if (__t != 0L) __t else null
}
case 4 => {
val __t = count
if (__t != 0L) __t else null
}
case 5 => sum.orNull
case 6 => {
val __t = scale
if (__t != 0) __t else null
}
case 7 => {
val __t = zeroCount
if (__t != 0L) __t else null
}
case 8 => positive.orNull
case 9 => negative.orNull
case 10 => {
val __t = flags
if (__t != 0) __t else null
}
case 11 => exemplars
case 12 => min.orNull
case 13 => max.orNull
}
}
def getField(__field: _root_.scalapb.descriptors.FieldDescriptor): _root_.scalapb.descriptors.PValue = {
_root_.scala.Predef.require(__field.containingMessage eq companion.scalaDescriptor)
(__field.number: @_root_.scala.unchecked) match {
case 1 => _root_.scalapb.descriptors.PRepeated(attributes.iterator.map(_.toPMessage).toVector)
case 2 => _root_.scalapb.descriptors.PLong(startTimeUnixNano)
case 3 => _root_.scalapb.descriptors.PLong(timeUnixNano)
case 4 => _root_.scalapb.descriptors.PLong(count)
case 5 => sum.map(_root_.scalapb.descriptors.PDouble(_)).getOrElse(_root_.scalapb.descriptors.PEmpty)
case 6 => _root_.scalapb.descriptors.PInt(scale)
case 7 => _root_.scalapb.descriptors.PLong(zeroCount)
case 8 => positive.map(_.toPMessage).getOrElse(_root_.scalapb.descriptors.PEmpty)
case 9 => negative.map(_.toPMessage).getOrElse(_root_.scalapb.descriptors.PEmpty)
case 10 => _root_.scalapb.descriptors.PInt(flags)
case 11 => _root_.scalapb.descriptors.PRepeated(exemplars.iterator.map(_.toPMessage).toVector)
case 12 => min.map(_root_.scalapb.descriptors.PDouble(_)).getOrElse(_root_.scalapb.descriptors.PEmpty)
case 13 => max.map(_root_.scalapb.descriptors.PDouble(_)).getOrElse(_root_.scalapb.descriptors.PEmpty)
}
}
def toProtoString: _root_.scala.Predef.String = _root_.scalapb.TextFormat.printToUnicodeString(this)
def companion: io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.type = io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint
// @@protoc_insertion_point(GeneratedMessage[opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint])
}
object ExponentialHistogramDataPoint extends scalapb.GeneratedMessageCompanion[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint] {
implicit def messageCompanion: scalapb.GeneratedMessageCompanion[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint] = this
def parseFrom(`_input__`: _root_.com.google.protobuf.CodedInputStream): io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint = {
val __attributes: _root_.scala.collection.immutable.VectorBuilder[io.opentelemetry.proto.common.v1.KeyValue] = new _root_.scala.collection.immutable.VectorBuilder[io.opentelemetry.proto.common.v1.KeyValue]
var __startTimeUnixNano: _root_.scala.Long = 0L
var __timeUnixNano: _root_.scala.Long = 0L
var __count: _root_.scala.Long = 0L
var __sum: _root_.scala.Option[_root_.scala.Double] = _root_.scala.None
var __scale: _root_.scala.Int = 0
var __zeroCount: _root_.scala.Long = 0L
var __positive: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] = _root_.scala.None
var __negative: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] = _root_.scala.None
var __flags: _root_.scala.Int = 0
val __exemplars: _root_.scala.collection.immutable.VectorBuilder[io.opentelemetry.proto.metrics.v1.Exemplar] = new _root_.scala.collection.immutable.VectorBuilder[io.opentelemetry.proto.metrics.v1.Exemplar]
var __min: _root_.scala.Option[_root_.scala.Double] = _root_.scala.None
var __max: _root_.scala.Option[_root_.scala.Double] = _root_.scala.None
var `_unknownFields__`: _root_.scalapb.UnknownFieldSet.Builder = null
var _done__ = false
while (!_done__) {
val _tag__ = _input__.readTag()
_tag__ match {
case 0 => _done__ = true
case 10 =>
__attributes += _root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.common.v1.KeyValue](_input__)
case 17 =>
__startTimeUnixNano = _input__.readFixed64()
case 25 =>
__timeUnixNano = _input__.readFixed64()
case 33 =>
__count = _input__.readFixed64()
case 41 =>
__sum = Option(_input__.readDouble())
case 48 =>
__scale = _input__.readSInt32()
case 57 =>
__zeroCount = _input__.readFixed64()
case 66 =>
__positive = Option(__positive.fold(_root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets](_input__))(_root_.scalapb.LiteParser.readMessage(_input__, _)))
case 74 =>
__negative = Option(__negative.fold(_root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets](_input__))(_root_.scalapb.LiteParser.readMessage(_input__, _)))
case 80 =>
__flags = _input__.readUInt32()
case 90 =>
__exemplars += _root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.metrics.v1.Exemplar](_input__)
case 97 =>
__min = Option(_input__.readDouble())
case 105 =>
__max = Option(_input__.readDouble())
case tag =>
if (_unknownFields__ == null) {
_unknownFields__ = new _root_.scalapb.UnknownFieldSet.Builder()
}
_unknownFields__.parseField(tag, _input__)
}
}
io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint(
attributes = __attributes.result(),
startTimeUnixNano = __startTimeUnixNano,
timeUnixNano = __timeUnixNano,
count = __count,
sum = __sum,
scale = __scale,
zeroCount = __zeroCount,
positive = __positive,
negative = __negative,
flags = __flags,
exemplars = __exemplars.result(),
min = __min,
max = __max,
unknownFields = if (_unknownFields__ == null) _root_.scalapb.UnknownFieldSet.empty else _unknownFields__.result()
)
}
implicit def messageReads: _root_.scalapb.descriptors.Reads[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint] = _root_.scalapb.descriptors.Reads{
case _root_.scalapb.descriptors.PMessage(__fieldsMap) =>
_root_.scala.Predef.require(__fieldsMap.keys.forall(_.containingMessage eq scalaDescriptor), "FieldDescriptor does not match message type.")
io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint(
attributes = __fieldsMap.get(scalaDescriptor.findFieldByNumber(1).get).map(_.as[_root_.scala.Seq[io.opentelemetry.proto.common.v1.KeyValue]]).getOrElse(_root_.scala.Seq.empty),
startTimeUnixNano = __fieldsMap.get(scalaDescriptor.findFieldByNumber(2).get).map(_.as[_root_.scala.Long]).getOrElse(0L),
timeUnixNano = __fieldsMap.get(scalaDescriptor.findFieldByNumber(3).get).map(_.as[_root_.scala.Long]).getOrElse(0L),
count = __fieldsMap.get(scalaDescriptor.findFieldByNumber(4).get).map(_.as[_root_.scala.Long]).getOrElse(0L),
sum = __fieldsMap.get(scalaDescriptor.findFieldByNumber(5).get).flatMap(_.as[_root_.scala.Option[_root_.scala.Double]]),
scale = __fieldsMap.get(scalaDescriptor.findFieldByNumber(6).get).map(_.as[_root_.scala.Int]).getOrElse(0),
zeroCount = __fieldsMap.get(scalaDescriptor.findFieldByNumber(7).get).map(_.as[_root_.scala.Long]).getOrElse(0L),
positive = __fieldsMap.get(scalaDescriptor.findFieldByNumber(8).get).flatMap(_.as[_root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets]]),
negative = __fieldsMap.get(scalaDescriptor.findFieldByNumber(9).get).flatMap(_.as[_root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets]]),
flags = __fieldsMap.get(scalaDescriptor.findFieldByNumber(10).get).map(_.as[_root_.scala.Int]).getOrElse(0),
exemplars = __fieldsMap.get(scalaDescriptor.findFieldByNumber(11).get).map(_.as[_root_.scala.Seq[io.opentelemetry.proto.metrics.v1.Exemplar]]).getOrElse(_root_.scala.Seq.empty),
min = __fieldsMap.get(scalaDescriptor.findFieldByNumber(12).get).flatMap(_.as[_root_.scala.Option[_root_.scala.Double]]),
max = __fieldsMap.get(scalaDescriptor.findFieldByNumber(13).get).flatMap(_.as[_root_.scala.Option[_root_.scala.Double]])
)
case _ => throw new RuntimeException("Expected PMessage")
}
def javaDescriptor: _root_.com.google.protobuf.Descriptors.Descriptor = MetricsProto.javaDescriptor.getMessageTypes().get(12)
def scalaDescriptor: _root_.scalapb.descriptors.Descriptor = MetricsProto.scalaDescriptor.messages(12)
def messageCompanionForFieldNumber(__number: _root_.scala.Int): _root_.scalapb.GeneratedMessageCompanion[_] = {
var __out: _root_.scalapb.GeneratedMessageCompanion[_] = null
(__number: @_root_.scala.unchecked) match {
case 1 => __out = io.opentelemetry.proto.common.v1.KeyValue
case 8 => __out = io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets
case 9 => __out = io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets
case 11 => __out = io.opentelemetry.proto.metrics.v1.Exemplar
}
__out
}
lazy val nestedMessagesCompanions: Seq[_root_.scalapb.GeneratedMessageCompanion[_ <: _root_.scalapb.GeneratedMessage]] =
Seq[_root_.scalapb.GeneratedMessageCompanion[_ <: _root_.scalapb.GeneratedMessage]](
_root_.io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets
)
def enumCompanionForFieldNumber(__fieldNumber: _root_.scala.Int): _root_.scalapb.GeneratedEnumCompanion[_] = throw new MatchError(__fieldNumber)
lazy val defaultInstance = io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint(
attributes = _root_.scala.Seq.empty,
startTimeUnixNano = 0L,
timeUnixNano = 0L,
count = 0L,
sum = _root_.scala.None,
scale = 0,
zeroCount = 0L,
positive = _root_.scala.None,
negative = _root_.scala.None,
flags = 0,
exemplars = _root_.scala.Seq.empty,
min = _root_.scala.None,
max = _root_.scala.None
)
/** Buckets are a set of bucket counts, encoded in a contiguous array
* of counts.
*
* @param offset
* Offset is the bucket index of the first entry in the bucket_counts array.
*
* Note: This uses a varint encoding as a simple form of compression.
* @param bucketCounts
* Count is an array of counts, where count[i] carries the count
* of the bucket at index (offset+i). count[i] is the count of
* values greater than or equal to base^(offset+i) and less than
* base^(offset+i+1).
*
* Note: By contrast, the explicit HistogramDataPoint uses
* fixed64. This field is expected to have many buckets,
* especially zeros, so uint64 has been selected to ensure
* varint encoding.
*/
@SerialVersionUID(0L)
final case class Buckets(
offset: _root_.scala.Int = 0,
bucketCounts: _root_.scala.Seq[_root_.scala.Long] = _root_.scala.Seq.empty,
unknownFields: _root_.scalapb.UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty
) extends scalapb.GeneratedMessage with scalapb.lenses.Updatable[Buckets] {
private[this] def bucketCountsSerializedSize = {
if (__bucketCountsSerializedSizeField == 0) __bucketCountsSerializedSizeField = {
var __s: _root_.scala.Int = 0
bucketCounts.foreach(__i => __s += _root_.com.google.protobuf.CodedOutputStream.computeUInt64SizeNoTag(__i))
__s
}
__bucketCountsSerializedSizeField
}
@transient private[this] var __bucketCountsSerializedSizeField: _root_.scala.Int = 0
@transient
private[this] var __serializedSizeMemoized: _root_.scala.Int = 0
private[this] def __computeSerializedSize(): _root_.scala.Int = {
var __size = 0
{
val __value = offset
if (__value != 0) {
__size += _root_.com.google.protobuf.CodedOutputStream.computeSInt32Size(1, __value)
}
};
if (bucketCounts.nonEmpty) {
val __localsize = bucketCountsSerializedSize
__size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__localsize) + __localsize
}
__size += unknownFields.serializedSize
__size
}
override def serializedSize: _root_.scala.Int = {
var __size = __serializedSizeMemoized
if (__size == 0) {
__size = __computeSerializedSize() + 1
__serializedSizeMemoized = __size
}
__size - 1
}
def writeTo(`_output__`: _root_.com.google.protobuf.CodedOutputStream): _root_.scala.Unit = {
{
val __v = offset
if (__v != 0) {
_output__.writeSInt32(1, __v)
}
};
if (bucketCounts.nonEmpty) {
_output__.writeTag(2, 2)
_output__.writeUInt32NoTag(bucketCountsSerializedSize)
bucketCounts.foreach(_output__.writeUInt64NoTag)
};
unknownFields.writeTo(_output__)
}
def withOffset(__v: _root_.scala.Int): Buckets = copy(offset = __v)
def clearBucketCounts = copy(bucketCounts = _root_.scala.Seq.empty)
def addBucketCounts(__vs: _root_.scala.Long *): Buckets = addAllBucketCounts(__vs)
def addAllBucketCounts(__vs: Iterable[_root_.scala.Long]): Buckets = copy(bucketCounts = bucketCounts ++ __vs)
def withBucketCounts(__v: _root_.scala.Seq[_root_.scala.Long]): Buckets = copy(bucketCounts = __v)
def withUnknownFields(__v: _root_.scalapb.UnknownFieldSet) = copy(unknownFields = __v)
def discardUnknownFields = copy(unknownFields = _root_.scalapb.UnknownFieldSet.empty)
def getFieldByNumber(__fieldNumber: _root_.scala.Int): _root_.scala.Any = {
(__fieldNumber: @_root_.scala.unchecked) match {
case 1 => {
val __t = offset
if (__t != 0) __t else null
}
case 2 => bucketCounts
}
}
def getField(__field: _root_.scalapb.descriptors.FieldDescriptor): _root_.scalapb.descriptors.PValue = {
_root_.scala.Predef.require(__field.containingMessage eq companion.scalaDescriptor)
(__field.number: @_root_.scala.unchecked) match {
case 1 => _root_.scalapb.descriptors.PInt(offset)
case 2 => _root_.scalapb.descriptors.PRepeated(bucketCounts.iterator.map(_root_.scalapb.descriptors.PLong(_)).toVector)
}
}
def toProtoString: _root_.scala.Predef.String = _root_.scalapb.TextFormat.printToUnicodeString(this)
def companion: io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets.type = io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets
// @@protoc_insertion_point(GeneratedMessage[opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets])
}
object Buckets extends scalapb.GeneratedMessageCompanion[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] {
implicit def messageCompanion: scalapb.GeneratedMessageCompanion[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] = this
def parseFrom(`_input__`: _root_.com.google.protobuf.CodedInputStream): io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets = {
var __offset: _root_.scala.Int = 0
val __bucketCounts: _root_.scala.collection.immutable.VectorBuilder[_root_.scala.Long] = new _root_.scala.collection.immutable.VectorBuilder[_root_.scala.Long]
var `_unknownFields__`: _root_.scalapb.UnknownFieldSet.Builder = null
var _done__ = false
while (!_done__) {
val _tag__ = _input__.readTag()
_tag__ match {
case 0 => _done__ = true
case 8 =>
__offset = _input__.readSInt32()
case 16 =>
__bucketCounts += _input__.readUInt64()
case 18 => {
val length = _input__.readRawVarint32()
val oldLimit = _input__.pushLimit(length)
while (_input__.getBytesUntilLimit > 0) {
__bucketCounts += _input__.readUInt64()
}
_input__.popLimit(oldLimit)
}
case tag =>
if (_unknownFields__ == null) {
_unknownFields__ = new _root_.scalapb.UnknownFieldSet.Builder()
}
_unknownFields__.parseField(tag, _input__)
}
}
io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets(
offset = __offset,
bucketCounts = __bucketCounts.result(),
unknownFields = if (_unknownFields__ == null) _root_.scalapb.UnknownFieldSet.empty else _unknownFields__.result()
)
}
implicit def messageReads: _root_.scalapb.descriptors.Reads[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] = _root_.scalapb.descriptors.Reads{
case _root_.scalapb.descriptors.PMessage(__fieldsMap) =>
_root_.scala.Predef.require(__fieldsMap.keys.forall(_.containingMessage eq scalaDescriptor), "FieldDescriptor does not match message type.")
io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets(
offset = __fieldsMap.get(scalaDescriptor.findFieldByNumber(1).get).map(_.as[_root_.scala.Int]).getOrElse(0),
bucketCounts = __fieldsMap.get(scalaDescriptor.findFieldByNumber(2).get).map(_.as[_root_.scala.Seq[_root_.scala.Long]]).getOrElse(_root_.scala.Seq.empty)
)
case _ => throw new RuntimeException("Expected PMessage")
}
def javaDescriptor: _root_.com.google.protobuf.Descriptors.Descriptor = io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.javaDescriptor.getNestedTypes().get(0)
def scalaDescriptor: _root_.scalapb.descriptors.Descriptor = io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.scalaDescriptor.nestedMessages(0)
def messageCompanionForFieldNumber(__number: _root_.scala.Int): _root_.scalapb.GeneratedMessageCompanion[_] = throw new MatchError(__number)
lazy val nestedMessagesCompanions: Seq[_root_.scalapb.GeneratedMessageCompanion[_ <: _root_.scalapb.GeneratedMessage]] = Seq.empty
def enumCompanionForFieldNumber(__fieldNumber: _root_.scala.Int): _root_.scalapb.GeneratedEnumCompanion[_] = throw new MatchError(__fieldNumber)
lazy val defaultInstance = io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets(
offset = 0,
bucketCounts = _root_.scala.Seq.empty
)
implicit class BucketsLens[UpperPB](_l: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets]) extends _root_.scalapb.lenses.ObjectLens[UpperPB, io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets](_l) {
def offset: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Int] = field(_.offset)((c_, f_) => c_.copy(offset = f_))
def bucketCounts: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Seq[_root_.scala.Long]] = field(_.bucketCounts)((c_, f_) => c_.copy(bucketCounts = f_))
}
final val OFFSET_FIELD_NUMBER = 1
final val BUCKET_COUNTS_FIELD_NUMBER = 2
def of(
offset: _root_.scala.Int,
bucketCounts: _root_.scala.Seq[_root_.scala.Long]
): _root_.io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets = _root_.io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets(
offset,
bucketCounts
)
// @@protoc_insertion_point(GeneratedMessageCompanion[opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets])
}
implicit class ExponentialHistogramDataPointLens[UpperPB](_l: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint]) extends _root_.scalapb.lenses.ObjectLens[UpperPB, io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint](_l) {
def attributes: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Seq[io.opentelemetry.proto.common.v1.KeyValue]] = field(_.attributes)((c_, f_) => c_.copy(attributes = f_))
def startTimeUnixNano: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Long] = field(_.startTimeUnixNano)((c_, f_) => c_.copy(startTimeUnixNano = f_))
def timeUnixNano: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Long] = field(_.timeUnixNano)((c_, f_) => c_.copy(timeUnixNano = f_))
def count: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Long] = field(_.count)((c_, f_) => c_.copy(count = f_))
def sum: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Double] = field(_.getSum)((c_, f_) => c_.copy(sum = Option(f_)))
def optionalSum: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Option[_root_.scala.Double]] = field(_.sum)((c_, f_) => c_.copy(sum = f_))
def scale: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Int] = field(_.scale)((c_, f_) => c_.copy(scale = f_))
def zeroCount: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Long] = field(_.zeroCount)((c_, f_) => c_.copy(zeroCount = f_))
def positive: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] = field(_.getPositive)((c_, f_) => c_.copy(positive = Option(f_)))
def optionalPositive: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets]] = field(_.positive)((c_, f_) => c_.copy(positive = f_))
def negative: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets] = field(_.getNegative)((c_, f_) => c_.copy(negative = Option(f_)))
def optionalNegative: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets]] = field(_.negative)((c_, f_) => c_.copy(negative = f_))
def flags: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Int] = field(_.flags)((c_, f_) => c_.copy(flags = f_))
def exemplars: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Seq[io.opentelemetry.proto.metrics.v1.Exemplar]] = field(_.exemplars)((c_, f_) => c_.copy(exemplars = f_))
def min: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Double] = field(_.getMin)((c_, f_) => c_.copy(min = Option(f_)))
def optionalMin: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Option[_root_.scala.Double]] = field(_.min)((c_, f_) => c_.copy(min = f_))
def max: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Double] = field(_.getMax)((c_, f_) => c_.copy(max = Option(f_)))
def optionalMax: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Option[_root_.scala.Double]] = field(_.max)((c_, f_) => c_.copy(max = f_))
}
final val ATTRIBUTES_FIELD_NUMBER = 1
final val START_TIME_UNIX_NANO_FIELD_NUMBER = 2
final val TIME_UNIX_NANO_FIELD_NUMBER = 3
final val COUNT_FIELD_NUMBER = 4
final val SUM_FIELD_NUMBER = 5
final val SCALE_FIELD_NUMBER = 6
final val ZERO_COUNT_FIELD_NUMBER = 7
final val POSITIVE_FIELD_NUMBER = 8
final val NEGATIVE_FIELD_NUMBER = 9
final val FLAGS_FIELD_NUMBER = 10
final val EXEMPLARS_FIELD_NUMBER = 11
final val MIN_FIELD_NUMBER = 12
final val MAX_FIELD_NUMBER = 13
def of(
attributes: _root_.scala.Seq[io.opentelemetry.proto.common.v1.KeyValue],
startTimeUnixNano: _root_.scala.Long,
timeUnixNano: _root_.scala.Long,
count: _root_.scala.Long,
sum: _root_.scala.Option[_root_.scala.Double],
scale: _root_.scala.Int,
zeroCount: _root_.scala.Long,
positive: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets],
negative: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets],
flags: _root_.scala.Int,
exemplars: _root_.scala.Seq[io.opentelemetry.proto.metrics.v1.Exemplar],
min: _root_.scala.Option[_root_.scala.Double],
max: _root_.scala.Option[_root_.scala.Double]
): _root_.io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint = _root_.io.opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint(
attributes,
startTimeUnixNano,
timeUnixNano,
count,
sum,
scale,
zeroCount,
positive,
negative,
flags,
exemplars,
min,
max
)
// @@protoc_insertion_point(GeneratedMessageCompanion[opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint])
}
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