final case class ExponentialHistogramDataPoint(attributes: Seq[KeyValue] = _root_.scala.Seq.empty, startTimeUnixNano: Long = 0L, timeUnixNano: Long = 0L, count: Long = 0L, sum: Option[Double] = _root_.scala.None, scale: Int = 0, zeroCount: Long = 0L, positive: Option[Buckets] = _root_.scala.None, negative: Option[Buckets] = _root_.scala.None, flags: Int = 0, exemplars: Seq[Exemplar] = _root_.scala.Seq.empty, min: Option[Double] = _root_.scala.None, max: Option[Double] = _root_.scala.None, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[ExponentialHistogramDataPoint] with Product with Serializable

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.

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).

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.

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.

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.

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

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 (baseindex) 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.

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).

positive

positive carries the positive range of exponential bucket counts.

negative

negative carries the negative range of exponential bucket counts.

flags

Flags that apply to this specific data point. See DataPointFlags for the available flags and their meaning.

exemplars

(Optional) List of exemplars collected from measurements that were used to form the data point

min

min is the minimum value over (start_time, end_time].

max

max is the maximum value over (start_time, end_time].

Annotations
@SerialVersionUID()
Linear Supertypes
Updatable[ExponentialHistogramDataPoint], GeneratedMessage, Serializable, Serializable, Product, Equals, AnyRef, Any
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  1. ExponentialHistogramDataPoint
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  5. Serializable
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  7. Equals
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Instance Constructors

  1. new ExponentialHistogramDataPoint(attributes: Seq[KeyValue] = _root_.scala.Seq.empty, startTimeUnixNano: Long = 0L, timeUnixNano: Long = 0L, count: Long = 0L, sum: Option[Double] = _root_.scala.None, scale: Int = 0, zeroCount: Long = 0L, positive: Option[Buckets] = _root_.scala.None, negative: Option[Buckets] = _root_.scala.None, flags: Int = 0, exemplars: Seq[Exemplar] = _root_.scala.Seq.empty, min: Option[Double] = _root_.scala.None, max: Option[Double] = _root_.scala.None, unknownFields: UnknownFieldSet = ...)

    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).

    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.

    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.

    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.

    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

    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 (baseindex) 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.

    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).

    positive

    positive carries the positive range of exponential bucket counts.

    negative

    negative carries the negative range of exponential bucket counts.

    flags

    Flags that apply to this specific data point. See DataPointFlags for the available flags and their meaning.

    exemplars

    (Optional) List of exemplars collected from measurements that were used to form the data point

    min

    min is the minimum value over (start_time, end_time].

    max

    max is the maximum value over (start_time, end_time].

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def addAllAttributes(__vs: Iterable[KeyValue]): ExponentialHistogramDataPoint
  5. def addAllExemplars(__vs: Iterable[Exemplar]): ExponentialHistogramDataPoint
  6. def addAttributes(__vs: KeyValue*): ExponentialHistogramDataPoint
  7. def addExemplars(__vs: Exemplar*): ExponentialHistogramDataPoint
  8. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  9. val attributes: Seq[KeyValue]
  10. def clearAttributes: ExponentialHistogramDataPoint
  11. def clearExemplars: ExponentialHistogramDataPoint
  12. def clearMax: ExponentialHistogramDataPoint
  13. def clearMin: ExponentialHistogramDataPoint
  14. def clearNegative: ExponentialHistogramDataPoint
  15. def clearPositive: ExponentialHistogramDataPoint
  16. def clearSum: ExponentialHistogramDataPoint
  17. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  18. def companion: ExponentialHistogramDataPoint.type
    Definition Classes
    ExponentialHistogramDataPoint → GeneratedMessage
  19. val count: Long
  20. def discardUnknownFields: ExponentialHistogramDataPoint
  21. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  22. val exemplars: Seq[Exemplar]
  23. val flags: Int
  24. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  25. def getField(__field: FieldDescriptor): PValue
    Definition Classes
    ExponentialHistogramDataPoint → GeneratedMessage
  26. def getFieldByNumber(__fieldNumber: Int): Any
    Definition Classes
    ExponentialHistogramDataPoint → GeneratedMessage
  27. def getMax: Double
  28. def getMin: Double
  29. def getNegative: Buckets
  30. def getPositive: Buckets
  31. def getSum: Double
  32. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  33. val max: Option[Double]
  34. val min: Option[Double]
  35. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  36. val negative: Option[Buckets]
  37. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  38. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  39. val positive: Option[Buckets]
  40. val scale: Int
  41. def serializedSize: Int
    Definition Classes
    ExponentialHistogramDataPoint → GeneratedMessage
  42. val startTimeUnixNano: Long
  43. val sum: Option[Double]
  44. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  45. val timeUnixNano: Long
  46. final def toByteArray: Array[Byte]
    Definition Classes
    GeneratedMessage
  47. final def toByteString: ByteString
    Definition Classes
    GeneratedMessage
  48. final def toPMessage: PMessage
    Definition Classes
    GeneratedMessage
  49. def toProtoString: String
    Definition Classes
    ExponentialHistogramDataPoint → GeneratedMessage
  50. val unknownFields: UnknownFieldSet
  51. def update(ms: (Lens[ExponentialHistogramDataPoint, ExponentialHistogramDataPoint]) ⇒ Mutation[ExponentialHistogramDataPoint]*): ExponentialHistogramDataPoint
    Definition Classes
    Updatable
  52. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  53. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  54. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  55. def withAttributes(__v: Seq[KeyValue]): ExponentialHistogramDataPoint
  56. def withCount(__v: Long): ExponentialHistogramDataPoint
  57. def withExemplars(__v: Seq[Exemplar]): ExponentialHistogramDataPoint
  58. def withFlags(__v: Int): ExponentialHistogramDataPoint
  59. def withMax(__v: Double): ExponentialHistogramDataPoint
  60. def withMin(__v: Double): ExponentialHistogramDataPoint
  61. def withNegative(__v: Buckets): ExponentialHistogramDataPoint
  62. def withPositive(__v: Buckets): ExponentialHistogramDataPoint
  63. def withScale(__v: Int): ExponentialHistogramDataPoint
  64. def withStartTimeUnixNano(__v: Long): ExponentialHistogramDataPoint
  65. def withSum(__v: Double): ExponentialHistogramDataPoint
  66. def withTimeUnixNano(__v: Long): ExponentialHistogramDataPoint
  67. def withUnknownFields(__v: UnknownFieldSet): ExponentialHistogramDataPoint
  68. def withZeroCount(__v: Long): ExponentialHistogramDataPoint
  69. final def writeDelimitedTo(output: OutputStream): Unit
    Definition Classes
    GeneratedMessage
  70. def writeTo(_output__: CodedOutputStream): Unit
    Definition Classes
    ExponentialHistogramDataPoint → GeneratedMessage
  71. final def writeTo(output: OutputStream): Unit
    Definition Classes
    GeneratedMessage
  72. val zeroCount: Long

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from Updatable[ExponentialHistogramDataPoint]

Inherited from GeneratedMessage

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

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