Type Members

1.  sealed abstract class AggregationTemporality extends GeneratedEnum

   AggregationTemporality defines how a metric aggregator reports aggregated values.

   AggregationTemporality defines how a metric aggregator reports aggregated values. It describes how those values relate to the time interval over which they are aggregated.

2.  sealed abstract class DataPointFlags extends GeneratedEnum

   DataPointFlags is defined as a protobuf 'uint32' type and is to be used as a bit-field representing 32 distinct boolean flags.

   DataPointFlags is defined as a protobuf 'uint32' type and is to be used as a bit-field representing 32 distinct boolean flags. Each flag defined in this enum is a bit-mask. To test the presence of a single flag in the flags of a data point, for example, use an expression like:

   (point.flags & FLAG_NO_RECORDED_VALUE) == FLAG_NO_RECORDED_VALUE

3.  final case class Exemplar(filteredAttributes: Seq[KeyValue] = _root_.scala.Seq.empty, timeUnixNano: Long = 0L, value: Value = ..., spanId: ByteString = ..., traceId: ByteString = ..., unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[Exemplar] with Product with Serializable

   A representation of an exemplar, which is a sample input measurement.

   A representation of an exemplar, which is a sample input measurement. Exemplars also hold information about the environment when the measurement was recorded, for example the span and trace ID of the active span when the exemplar was recorded.

   filteredAttributes

   The set of key/value pairs that were filtered out by the aggregator, but recorded alongside the original measurement. Only key/value pairs that were filtered out by the aggregator should be included

   timeUnixNano

   time_unix_nano is the exact time when this exemplar was recorded Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January 1970.

   spanId

   (Optional) Span ID of the exemplar trace. span_id may be missing if the measurement is not recorded inside a trace or if the trace is not sampled.

   traceId

   (Optional) Trace ID of the exemplar trace. trace_id may be missing if the measurement is not recorded inside a trace or if the trace is not sampled.

   Annotations

   @SerialVersionUID()

4.  final case class ExponentialHistogram(dataPoints: Seq[ExponentialHistogramDataPoint] = _root_.scala.Seq.empty, aggregationTemporality: AggregationTemporality = ..., unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[ExponentialHistogram] with Product with Serializable

   ExponentialHistogram represents the type of a metric that is calculated by aggregating as a ExponentialHistogram of all reported double measurements over a time interval.

   ExponentialHistogram represents the type of a metric that is calculated by aggregating as a ExponentialHistogram of all reported double measurements over a time interval.

   aggregationTemporality

   aggregation_temporality describes if the aggregator reports delta changes since last report time, or cumulative changes since a fixed start time.

   Annotations

   @SerialVersionUID()

5.  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.

   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 (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.

   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()

6.  final case class Gauge(dataPoints: Seq[NumberDataPoint] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[Gauge] with Product with Serializable

   Gauge represents the type of a scalar metric that always exports the "current value" for every data point.

   Gauge represents the type of a scalar metric that always exports the "current value" for every data point. It should be used for an "unknown" aggregation.

   A Gauge does not support different aggregation temporalities. Given the aggregation is unknown, points cannot be combined using the same aggregation, regardless of aggregation temporalities. Therefore, AggregationTemporality is not included. Consequently, this also means "StartTimeUnixNano" is ignored for all data points.

   Annotations

   @SerialVersionUID()

7.  final case class Histogram(dataPoints: Seq[HistogramDataPoint] = _root_.scala.Seq.empty, aggregationTemporality: AggregationTemporality = ..., unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[Histogram] with Product with Serializable

   Histogram represents the type of a metric that is calculated by aggregating as a Histogram of all reported measurements over a time interval.

   Histogram represents the type of a metric that is calculated by aggregating as a Histogram of all reported measurements over a time interval.

   aggregationTemporality

   aggregation_temporality describes if the aggregator reports delta changes since last report time, or cumulative changes since a fixed start time.

   Annotations

   @SerialVersionUID()

8.  final case class HistogramDataPoint(attributes: Seq[KeyValue] = _root_.scala.Seq.empty, startTimeUnixNano: Long = 0L, timeUnixNano: Long = 0L, count: Long = 0L, sum: Option[Double] = _root_.scala.None, bucketCounts: Seq[Long] = _root_.scala.Seq.empty, explicitBounds: Seq[Double] = _root_.scala.Seq.empty, exemplars: Seq[Exemplar] = _root_.scala.Seq.empty, flags: Int = 0, min: Option[Double] = _root_.scala.None, max: Option[Double] = _root_.scala.None, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[HistogramDataPoint] with Product with Serializable

   HistogramDataPoint is a single data point in a timeseries that describes the time-varying values of a Histogram.

   HistogramDataPoint is a single data point in a timeseries that describes the time-varying values of a Histogram. A Histogram contains summary statistics for a population of values, it may optionally contain the distribution of those values across a set of buckets.

   If the histogram contains the distribution of values, then both "explicit_bounds" and "bucket counts" fields must be defined. If the histogram does not contain the distribution of values, then both "explicit_bounds" and "bucket_counts" must be omitted and only "count" and "sum" are known.

   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 "count" fields in buckets if a histogram is provided.

   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

   bucketCounts

   bucket_counts is an optional field contains the count values of histogram for each bucket. The sum of the bucket_counts must equal the value in the count field. The number of elements in bucket_counts array must be by one greater than the number of elements in explicit_bounds array.

   explicitBounds

   explicit_bounds specifies buckets with explicitly defined bounds for values. The boundaries for bucket at index i are: (-infinity, explicit_bounds[i]] for i == 0 (explicit_bounds[i-1], explicit_bounds[i]] for 0 < i < size(explicit_bounds) (explicit_bounds[i-1], +infinity) for i == size(explicit_bounds) The values in the explicit_bounds array must be strictly increasing. Histogram buckets are inclusive of their upper boundary, except the last bucket where the boundary is at infinity. This format is intentionally compatible with the OpenMetrics histogram definition.

   exemplars

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

   flags

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

   min

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

   max

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

   Annotations

   @SerialVersionUID()

9.  final case class Metric(name: String = "", description: String = "", unit: String = "", data: Data = ..., unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[Metric] with Product with Serializable

   Defines a Metric which has one or more timeseries.

   Defines a Metric which has one or more timeseries. The following is a brief summary of the Metric data model. For more details, see:

   https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/datamodel.md

   The data model and relation between entities is shown in the diagram below. Here, "DataPoint" is the term used to refer to any one of the specific data point value types, and "points" is the term used to refer to any one of the lists of points contained in the Metric.

   - Metric is composed of a metadata and data. - Metadata part contains a name, description, unit. - Data is one of the possible types (Sum, Gauge, Histogram, Summary). - DataPoint contains timestamps, attributes, and one of the possible value type fields.

   Metric +------------+ |name | |description | |unit | +------------------------------------+ |data |---> |Gauge, Sum, Histogram, Summary, ... | +------------+ +------------------------------------+

   Data [One of Gauge, Sum, Histogram, Summary, ...] +-----------+ |... | // Metadata about the Data. |points |--+ +-----------+ | | +---------------------------+ | |DataPoint 1 | v |+------+------+ +------+ | +-----+ ||label |label |...|label | | | 1 |-->||value1|value2|...|valueN| | +-----+ |+------+------+ +------+ | | . | |+-----+ | | . | ||value| | | . | |+-----+ | | . | +---------------------------+ | . | . | . | . | . | . | . | +---------------------------+ | . | |DataPoint M | +-----+ |+------+------+ +------+ | | M |-->||label |label |...|label | | +-----+ ||value1|value2|...|valueN| | |+------+------+ +------+ | |+-----+ | ||value| | |+-----+ | +---------------------------+

   Each distinct type of DataPoint represents the output of a specific aggregation function, the result of applying the DataPoint's associated function of to one or more measurements.

   All DataPoint types have three common fields: - Attributes includes key-value pairs associated with the data point - TimeUnixNano is required, set to the end time of the aggregation - StartTimeUnixNano is optional, but strongly encouraged for DataPoints having an AggregationTemporality field, as discussed below.

   Both TimeUnixNano and StartTimeUnixNano values are expressed as UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January 1970.

   # TimeUnixNano

   This field is required, having consistent interpretation across DataPoint types. TimeUnixNano is the moment corresponding to when the data point's aggregate value was captured.

   Data points with the 0 value for TimeUnixNano SHOULD be rejected by consumers.

   # StartTimeUnixNano

   StartTimeUnixNano in general allows detecting when a sequence of observations is unbroken. This field indicates to consumers the start time for points with cumulative and delta AggregationTemporality, and it should be included whenever possible to support correct rate calculation. Although it may be omitted when the start time is truly unknown, setting StartTimeUnixNano is strongly encouraged.

   name

   name of the metric, including its DNS name prefix. It must be unique.

   description

   description of the metric, which can be used in documentation.

   unit

   unit in which the metric value is reported. Follows the format described by http://unitsofmeasure.org/ucum.html.

   Annotations

   @SerialVersionUID()

10.  final case class MetricsData(resourceMetrics: Seq[ResourceMetrics] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[MetricsData] with Product with Serializable

    MetricsData represents the metrics data that can be stored in a persistent storage, OR can be embedded by other protocols that transfer OTLP metrics data but do not implement the OTLP protocol.

    MetricsData represents the metrics data that can be stored in a persistent storage, OR can be embedded by other protocols that transfer OTLP metrics data but do not implement the OTLP protocol.

    The main difference between this message and collector protocol is that in this message there will not be any "control" or "metadata" specific to OTLP protocol.

    When new fields are added into this message, the OTLP request MUST be updated as well.

    resourceMetrics

    An array of ResourceMetrics. For data coming from a single resource this array will typically contain one element. Intermediary nodes that receive data from multiple origins typically batch the data before forwarding further and in that case this array will contain multiple elements.

    Annotations

    @SerialVersionUID()

11.  final case class NumberDataPoint(attributes: Seq[KeyValue] = _root_.scala.Seq.empty, startTimeUnixNano: Long = 0L, timeUnixNano: Long = 0L, value: Value = ..., exemplars: Seq[Exemplar] = _root_.scala.Seq.empty, flags: Int = 0, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[NumberDataPoint] with Product with Serializable

    NumberDataPoint is a single data point in a timeseries that describes the time-varying scalar value of a metric.

    NumberDataPoint is a single data point in a timeseries that describes the time-varying scalar value of a metric.

    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.

    exemplars

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

    flags

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

    Annotations

    @SerialVersionUID()

12.  final case class ResourceMetrics(resource: Option[Resource] = _root_.scala.None, scopeMetrics: Seq[ScopeMetrics] = _root_.scala.Seq.empty, instrumentationLibraryMetrics: Seq[InstrumentationLibraryMetrics] = _root_.scala.Seq.empty, schemaUrl: String = "", unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[ResourceMetrics] with Product with Serializable

    A collection of ScopeMetrics from a Resource.

    A collection of ScopeMetrics from a Resource.

    resource

    The resource for the metrics in this message. If this field is not set then no resource info is known.

    scopeMetrics

    A list of metrics that originate from a resource.

    instrumentationLibraryMetrics

    A list of InstrumentationLibraryMetrics that originate from a resource. This field is deprecated and will be removed after grace period expires on June 15, 2022. During the grace period the following rules SHOULD be followed: For Binary Protobufs

    Binary Protobuf senders SHOULD NOT set instrumentation_library_metrics. Instead scope_metrics SHOULD be set. Binary Protobuf receivers SHOULD check if instrumentation_library_metrics is set and scope_metrics is not set then the value in instrumentation_library_metrics SHOULD be used instead by converting InstrumentationLibraryMetrics into ScopeMetrics. If scope_metrics is set then instrumentation_library_metrics SHOULD be ignored. For JSON

    JSON senders that set instrumentation_library_metrics field MAY also set scope_metrics to carry the same metrics, essentially double-publishing the same data. Such double-publishing MAY be controlled by a user-settable option. If double-publishing is not used then the senders SHOULD set scope_metrics and SHOULD NOT set instrumentation_library_metrics. JSON receivers SHOULD check if instrumentation_library_metrics is set and scope_metrics is not set then the value in instrumentation_library_metrics SHOULD be used instead by converting InstrumentationLibraryMetrics into ScopeMetrics. If scope_metrics is set then instrumentation_library_metrics field SHOULD be ignored.

    schemaUrl

    This schema_url applies to the data in the "resource" field. It does not apply to the data in the "scope_metrics" field which have their own schema_url field.

    Annotations

    @SerialVersionUID()

13.  final case class ScopeMetrics(scope: Option[InstrumentationScope] = _root_.scala.None, metrics: Seq[Metric] = _root_.scala.Seq.empty, schemaUrl: String = "", unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[ScopeMetrics] with Product with Serializable

    A collection of Metrics produced by an Scope.

    A collection of Metrics produced by an Scope.

    scope

    The instrumentation scope information for the metrics in this message. Semantically when InstrumentationScope isn't set, it is equivalent with an empty instrumentation scope name (unknown).

    metrics

    A list of metrics that originate from an instrumentation library.

    schemaUrl

    This schema_url applies to all metrics in the "metrics" field.

    Annotations

    @SerialVersionUID()

14.  final case class Sum(dataPoints: Seq[NumberDataPoint] = _root_.scala.Seq.empty, aggregationTemporality: AggregationTemporality = ..., isMonotonic: Boolean = false, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[Sum] with Product with Serializable

    Sum represents the type of a scalar metric that is calculated as a sum of all reported measurements over a time interval.

    Sum represents the type of a scalar metric that is calculated as a sum of all reported measurements over a time interval.

    aggregationTemporality

    aggregation_temporality describes if the aggregator reports delta changes since last report time, or cumulative changes since a fixed start time.

    isMonotonic

    If "true" means that the sum is monotonic.

    Annotations

    @SerialVersionUID()

15.  final case class Summary(dataPoints: Seq[SummaryDataPoint] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[Summary] with Product with Serializable

    Summary metric data are used to convey quantile summaries, a Prometheus (see: https://prometheus.io/docs/concepts/metric_types/#summary) and OpenMetrics (see: https://github.com/OpenObservability/OpenMetrics/blob/4dbf6075567ab43296eed941037c12951faafb92/protos/prometheus.proto#L45) data type.

    Summary metric data are used to convey quantile summaries, a Prometheus (see: https://prometheus.io/docs/concepts/metric_types/#summary) and OpenMetrics (see: https://github.com/OpenObservability/OpenMetrics/blob/4dbf6075567ab43296eed941037c12951faafb92/protos/prometheus.proto#L45) data type. These data points cannot always be merged in a meaningful way. While they can be useful in some applications, histogram data points are recommended for new applications.

    Annotations

    @SerialVersionUID()

16.  final case class SummaryDataPoint(attributes: Seq[KeyValue] = _root_.scala.Seq.empty, startTimeUnixNano: Long = 0L, timeUnixNano: Long = 0L, count: Long = 0L, sum: Double = 0.0, quantileValues: Seq[ValueAtQuantile] = _root_.scala.Seq.empty, flags: Int = 0, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[SummaryDataPoint] with Product with Serializable

    SummaryDataPoint is a single data point in a timeseries that describes the time-varying values of a Summary metric.

    SummaryDataPoint is a single data point in a timeseries that describes the time-varying values of a Summary metric.

    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.

    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#summary

    quantileValues

    (Optional) list of values at different quantiles of the distribution calculated from the current snapshot. The quantiles must be strictly increasing.

    flags

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

    Annotations

    @SerialVersionUID()

17.  final case class InstrumentationLibraryMetrics(instrumentationLibrary: Option[InstrumentationLibrary] = _root_.scala.None, metrics: Seq[Metric] = _root_.scala.Seq.empty, schemaUrl: String = "", unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[InstrumentationLibraryMetrics] with Product with Serializable

    A collection of Metrics produced by an InstrumentationLibrary.

    A collection of Metrics produced by an InstrumentationLibrary. InstrumentationLibraryMetrics is wire-compatible with ScopeMetrics for binary Protobuf format. This message is deprecated and will be removed on June 15, 2022.

    instrumentationLibrary

    The instrumentation library information for the metrics in this message. Semantically when InstrumentationLibrary isn't set, it is equivalent with an empty instrumentation library name (unknown).

    metrics

    A list of metrics that originate from an instrumentation library.

    schemaUrl

    This schema_url applies to all metrics in the "metrics" field.

    Annotations

    @SerialVersionUID() @deprecated

    Deprecated

    (Since version ) Marked as deprecated in proto file