opentelemetry_proto/proto/tonic/
opentelemetry.proto.metrics.v1.rs

1// This file is @generated by prost-build.
2/// MetricsData represents the metrics data that can be stored in a persistent
3/// storage, OR can be embedded by other protocols that transfer OTLP metrics
4/// data but do not implement the OTLP protocol.
5///
6/// The main difference between this message and collector protocol is that
7/// in this message there will not be any "control" or "metadata" specific to
8/// OTLP protocol.
9///
10/// When new fields are added into this message, the OTLP request MUST be updated
11/// as well.
12#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
13#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
14#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
15#[allow(clippy::derive_partial_eq_without_eq)]
16#[derive(Clone, PartialEq, ::prost::Message)]
17pub struct MetricsData {
18    /// An array of ResourceMetrics.
19    /// For data coming from a single resource this array will typically contain
20    /// one element. Intermediary nodes that receive data from multiple origins
21    /// typically batch the data before forwarding further and in that case this
22    /// array will contain multiple elements.
23    #[prost(message, repeated, tag = "1")]
24    pub resource_metrics: ::prost::alloc::vec::Vec<ResourceMetrics>,
25}
26/// A collection of ScopeMetrics from a Resource.
27#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
28#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
29#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
30#[allow(clippy::derive_partial_eq_without_eq)]
31#[derive(Clone, PartialEq, ::prost::Message)]
32pub struct ResourceMetrics {
33    /// The resource for the metrics in this message.
34    /// If this field is not set then no resource info is known.
35    #[prost(message, optional, tag = "1")]
36    pub resource: ::core::option::Option<super::super::resource::v1::Resource>,
37    /// A list of metrics that originate from a resource.
38    #[prost(message, repeated, tag = "2")]
39    pub scope_metrics: ::prost::alloc::vec::Vec<ScopeMetrics>,
40    /// The Schema URL, if known. This is the identifier of the Schema that the resource data
41    /// is recorded in. To learn more about Schema URL see
42    /// <https://opentelemetry.io/docs/specs/otel/schemas/#schema-url>
43    /// This schema_url applies to the data in the "resource" field. It does not apply
44    /// to the data in the "scope_metrics" field which have their own schema_url field.
45    #[prost(string, tag = "3")]
46    pub schema_url: ::prost::alloc::string::String,
47}
48/// A collection of Metrics produced by an Scope.
49#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
50#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
51#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
52#[allow(clippy::derive_partial_eq_without_eq)]
53#[derive(Clone, PartialEq, ::prost::Message)]
54pub struct ScopeMetrics {
55    /// The instrumentation scope information for the metrics in this message.
56    /// Semantically when InstrumentationScope isn't set, it is equivalent with
57    /// an empty instrumentation scope name (unknown).
58    #[prost(message, optional, tag = "1")]
59    pub scope: ::core::option::Option<super::super::common::v1::InstrumentationScope>,
60    /// A list of metrics that originate from an instrumentation library.
61    #[prost(message, repeated, tag = "2")]
62    pub metrics: ::prost::alloc::vec::Vec<Metric>,
63    /// The Schema URL, if known. This is the identifier of the Schema that the metric data
64    /// is recorded in. To learn more about Schema URL see
65    /// <https://opentelemetry.io/docs/specs/otel/schemas/#schema-url>
66    /// This schema_url applies to all metrics in the "metrics" field.
67    #[prost(string, tag = "3")]
68    pub schema_url: ::prost::alloc::string::String,
69}
70/// Defines a Metric which has one or more timeseries.  The following is a
71/// brief summary of the Metric data model.  For more details, see:
72///
73///    <https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/data-model.md>
74///
75///
76/// The data model and relation between entities is shown in the
77/// diagram below. Here, "DataPoint" is the term used to refer to any
78/// one of the specific data point value types, and "points" is the term used
79/// to refer to any one of the lists of points contained in the Metric.
80///
81/// - Metric is composed of a metadata and data.
82/// - Metadata part contains a name, description, unit.
83/// - Data is one of the possible types (Sum, Gauge, Histogram, Summary).
84/// - DataPoint contains timestamps, attributes, and one of the possible value type
85///    fields.
86///
87///      Metric
88///   +------------+
89///   |name        |
90///   |description |
91///   |unit        |     +------------------------------------+
92///   |data        |---> |Gauge, Sum, Histogram, Summary, ... |
93///   +------------+     +------------------------------------+
94///
95///     Data \[One of Gauge, Sum, Histogram, Summary, ...\]
96///   +-----------+
97///   |...        |  // Metadata about the Data.
98///   |points     |--+
99///   +-----------+  |
100///                  |      +---------------------------+
101///                  |      |DataPoint 1                |
102///                  v      |+------+------+   +------+ |
103///               +-----+   ||label |label |...|label | |
104///               |  1  |-->||value1|value2|...|valueN| |
105///               +-----+   |+------+------+   +------+ |
106///               |  .  |   |+-----+                    |
107///               |  .  |   ||value|                    |
108///               |  .  |   |+-----+                    |
109///               |  .  |   +---------------------------+
110///               |  .  |                   .
111///               |  .  |                   .
112///               |  .  |                   .
113///               |  .  |   +---------------------------+
114///               |  .  |   |DataPoint M                |
115///               +-----+   |+------+------+   +------+ |
116///               |  M  |-->||label |label |...|label | |
117///               +-----+   ||value1|value2|...|valueN| |
118///                         |+------+------+   +------+ |
119///                         |+-----+                    |
120///                         ||value|                    |
121///                         |+-----+                    |
122///                         +---------------------------+
123///
124/// Each distinct type of DataPoint represents the output of a specific
125/// aggregation function, the result of applying the DataPoint's
126/// associated function of to one or more measurements.
127///
128/// All DataPoint types have three common fields:
129/// - Attributes includes key-value pairs associated with the data point
130/// - TimeUnixNano is required, set to the end time of the aggregation
131/// - StartTimeUnixNano is optional, but strongly encouraged for DataPoints
132///    having an AggregationTemporality field, as discussed below.
133///
134/// Both TimeUnixNano and StartTimeUnixNano values are expressed as
135/// UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January 1970.
136///
137/// # TimeUnixNano
138///
139/// This field is required, having consistent interpretation across
140/// DataPoint types.  TimeUnixNano is the moment corresponding to when
141/// the data point's aggregate value was captured.
142///
143/// Data points with the 0 value for TimeUnixNano SHOULD be rejected
144/// by consumers.
145///
146/// # StartTimeUnixNano
147///
148/// StartTimeUnixNano in general allows detecting when a sequence of
149/// observations is unbroken.  This field indicates to consumers the
150/// start time for points with cumulative and delta
151/// AggregationTemporality, and it should be included whenever possible
152/// to support correct rate calculation.  Although it may be omitted
153/// when the start time is truly unknown, setting StartTimeUnixNano is
154/// strongly encouraged.
155#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
156#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
157#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
158#[allow(clippy::derive_partial_eq_without_eq)]
159#[derive(Clone, PartialEq, ::prost::Message)]
160pub struct Metric {
161    /// name of the metric.
162    #[prost(string, tag = "1")]
163    pub name: ::prost::alloc::string::String,
164    /// description of the metric, which can be used in documentation.
165    #[prost(string, tag = "2")]
166    pub description: ::prost::alloc::string::String,
167    /// unit in which the metric value is reported. Follows the format
168    /// described by <http://unitsofmeasure.org/ucum.html.>
169    #[prost(string, tag = "3")]
170    pub unit: ::prost::alloc::string::String,
171    /// Additional metadata attributes that describe the metric. \[Optional\].
172    /// Attributes are non-identifying.
173    /// Consumers SHOULD NOT need to be aware of these attributes.
174    /// These attributes MAY be used to encode information allowing
175    /// for lossless roundtrip translation to / from another data model.
176    /// Attribute keys MUST be unique (it is not allowed to have more than one
177    /// attribute with the same key).
178    #[prost(message, repeated, tag = "12")]
179    pub metadata: ::prost::alloc::vec::Vec<super::super::common::v1::KeyValue>,
180    /// Data determines the aggregation type (if any) of the metric, what is the
181    /// reported value type for the data points, as well as the relatationship to
182    /// the time interval over which they are reported.
183    #[prost(oneof = "metric::Data", tags = "5, 7, 9, 10, 11")]
184    pub data: ::core::option::Option<metric::Data>,
185}
186/// Nested message and enum types in `Metric`.
187pub mod metric {
188    /// Data determines the aggregation type (if any) of the metric, what is the
189    /// reported value type for the data points, as well as the relatationship to
190    /// the time interval over which they are reported.
191    #[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
192    #[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
193    #[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
194    #[allow(clippy::derive_partial_eq_without_eq)]
195    #[derive(Clone, PartialEq, ::prost::Oneof)]
196    pub enum Data {
197        #[prost(message, tag = "5")]
198        Gauge(super::Gauge),
199        #[prost(message, tag = "7")]
200        Sum(super::Sum),
201        #[prost(message, tag = "9")]
202        Histogram(super::Histogram),
203        #[prost(message, tag = "10")]
204        ExponentialHistogram(super::ExponentialHistogram),
205        #[prost(message, tag = "11")]
206        Summary(super::Summary),
207    }
208}
209/// Gauge represents the type of a scalar metric that always exports the
210/// "current value" for every data point. It should be used for an "unknown"
211/// aggregation.
212///
213/// A Gauge does not support different aggregation temporalities. Given the
214/// aggregation is unknown, points cannot be combined using the same
215/// aggregation, regardless of aggregation temporalities. Therefore,
216/// AggregationTemporality is not included. Consequently, this also means
217/// "StartTimeUnixNano" is ignored for all data points.
218#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
219#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
220#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
221#[allow(clippy::derive_partial_eq_without_eq)]
222#[derive(Clone, PartialEq, ::prost::Message)]
223pub struct Gauge {
224    #[prost(message, repeated, tag = "1")]
225    pub data_points: ::prost::alloc::vec::Vec<NumberDataPoint>,
226}
227/// Sum represents the type of a scalar metric that is calculated as a sum of all
228/// reported measurements over a time interval.
229#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
230#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
231#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
232#[allow(clippy::derive_partial_eq_without_eq)]
233#[derive(Clone, PartialEq, ::prost::Message)]
234pub struct Sum {
235    #[prost(message, repeated, tag = "1")]
236    pub data_points: ::prost::alloc::vec::Vec<NumberDataPoint>,
237    /// aggregation_temporality describes if the aggregator reports delta changes
238    /// since last report time, or cumulative changes since a fixed start time.
239    #[prost(enumeration = "AggregationTemporality", tag = "2")]
240    pub aggregation_temporality: i32,
241    /// If "true" means that the sum is monotonic.
242    #[prost(bool, tag = "3")]
243    pub is_monotonic: bool,
244}
245/// Histogram represents the type of a metric that is calculated by aggregating
246/// as a Histogram of all reported measurements over a time interval.
247#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
248#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
249#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
250#[allow(clippy::derive_partial_eq_without_eq)]
251#[derive(Clone, PartialEq, ::prost::Message)]
252pub struct Histogram {
253    #[prost(message, repeated, tag = "1")]
254    pub data_points: ::prost::alloc::vec::Vec<HistogramDataPoint>,
255    /// aggregation_temporality describes if the aggregator reports delta changes
256    /// since last report time, or cumulative changes since a fixed start time.
257    #[prost(enumeration = "AggregationTemporality", tag = "2")]
258    pub aggregation_temporality: i32,
259}
260/// ExponentialHistogram represents the type of a metric that is calculated by aggregating
261/// as a ExponentialHistogram of all reported double measurements over a time interval.
262#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
263#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
264#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
265#[allow(clippy::derive_partial_eq_without_eq)]
266#[derive(Clone, PartialEq, ::prost::Message)]
267pub struct ExponentialHistogram {
268    #[prost(message, repeated, tag = "1")]
269    pub data_points: ::prost::alloc::vec::Vec<ExponentialHistogramDataPoint>,
270    /// aggregation_temporality describes if the aggregator reports delta changes
271    /// since last report time, or cumulative changes since a fixed start time.
272    #[prost(enumeration = "AggregationTemporality", tag = "2")]
273    pub aggregation_temporality: i32,
274}
275/// Summary metric data are used to convey quantile summaries,
276/// a Prometheus (see: <https://prometheus.io/docs/concepts/metric_types/#summary>)
277/// and OpenMetrics (see: <https://github.com/OpenObservability/OpenMetrics/blob/4dbf6075567ab43296eed941037c12951faafb92/protos/prometheus.proto#L45>)
278/// data type. These data points cannot always be merged in a meaningful way.
279/// While they can be useful in some applications, histogram data points are
280/// recommended for new applications.
281#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
282#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
283#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
284#[allow(clippy::derive_partial_eq_without_eq)]
285#[derive(Clone, PartialEq, ::prost::Message)]
286pub struct Summary {
287    #[prost(message, repeated, tag = "1")]
288    pub data_points: ::prost::alloc::vec::Vec<SummaryDataPoint>,
289}
290/// NumberDataPoint is a single data point in a timeseries that describes the
291/// time-varying scalar value of a metric.
292#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
293#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
294#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
295#[allow(clippy::derive_partial_eq_without_eq)]
296#[derive(Clone, PartialEq, ::prost::Message)]
297pub struct NumberDataPoint {
298    /// The set of key/value pairs that uniquely identify the timeseries from
299    /// where this point belongs. The list may be empty (may contain 0 elements).
300    /// Attribute keys MUST be unique (it is not allowed to have more than one
301    /// attribute with the same key).
302    #[prost(message, repeated, tag = "7")]
303    pub attributes: ::prost::alloc::vec::Vec<super::super::common::v1::KeyValue>,
304    /// StartTimeUnixNano is optional but strongly encouraged, see the
305    /// the detailed comments above Metric.
306    ///
307    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
308    /// 1970.
309    #[prost(fixed64, tag = "2")]
310    pub start_time_unix_nano: u64,
311    /// TimeUnixNano is required, see the detailed comments above Metric.
312    ///
313    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
314    /// 1970.
315    #[prost(fixed64, tag = "3")]
316    pub time_unix_nano: u64,
317    /// (Optional) List of exemplars collected from
318    /// measurements that were used to form the data point
319    #[prost(message, repeated, tag = "5")]
320    pub exemplars: ::prost::alloc::vec::Vec<Exemplar>,
321    /// Flags that apply to this specific data point.  See DataPointFlags
322    /// for the available flags and their meaning.
323    #[prost(uint32, tag = "8")]
324    pub flags: u32,
325    /// The value itself.  A point is considered invalid when one of the recognized
326    /// value fields is not present inside this oneof.
327    #[prost(oneof = "number_data_point::Value", tags = "4, 6")]
328    pub value: ::core::option::Option<number_data_point::Value>,
329}
330/// Nested message and enum types in `NumberDataPoint`.
331pub mod number_data_point {
332    /// The value itself.  A point is considered invalid when one of the recognized
333    /// value fields is not present inside this oneof.
334    #[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
335    #[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
336    #[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
337    #[allow(clippy::derive_partial_eq_without_eq)]
338    #[derive(Clone, Copy, PartialEq, ::prost::Oneof)]
339    pub enum Value {
340        #[prost(double, tag = "4")]
341        AsDouble(f64),
342        #[prost(sfixed64, tag = "6")]
343        AsInt(i64),
344    }
345}
346/// HistogramDataPoint is a single data point in a timeseries that describes the
347/// time-varying values of a Histogram. A Histogram contains summary statistics
348/// for a population of values, it may optionally contain the distribution of
349/// those values across a set of buckets.
350///
351/// If the histogram contains the distribution of values, then both
352/// "explicit_bounds" and "bucket counts" fields must be defined.
353/// If the histogram does not contain the distribution of values, then both
354/// "explicit_bounds" and "bucket_counts" must be omitted and only "count" and
355/// "sum" are known.
356#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
357#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
358#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
359#[allow(clippy::derive_partial_eq_without_eq)]
360#[derive(Clone, PartialEq, ::prost::Message)]
361pub struct HistogramDataPoint {
362    /// The set of key/value pairs that uniquely identify the timeseries from
363    /// where this point belongs. The list may be empty (may contain 0 elements).
364    /// Attribute keys MUST be unique (it is not allowed to have more than one
365    /// attribute with the same key).
366    #[prost(message, repeated, tag = "9")]
367    pub attributes: ::prost::alloc::vec::Vec<super::super::common::v1::KeyValue>,
368    /// StartTimeUnixNano is optional but strongly encouraged, see the
369    /// the detailed comments above Metric.
370    ///
371    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
372    /// 1970.
373    #[prost(fixed64, tag = "2")]
374    pub start_time_unix_nano: u64,
375    /// TimeUnixNano is required, see the detailed comments above Metric.
376    ///
377    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
378    /// 1970.
379    #[prost(fixed64, tag = "3")]
380    pub time_unix_nano: u64,
381    /// count is the number of values in the population. Must be non-negative. This
382    /// value must be equal to the sum of the "count" fields in buckets if a
383    /// histogram is provided.
384    #[prost(fixed64, tag = "4")]
385    pub count: u64,
386    /// sum of the values in the population. If count is zero then this field
387    /// must be zero.
388    ///
389    /// Note: Sum should only be filled out when measuring non-negative discrete
390    /// events, and is assumed to be monotonic over the values of these events.
391    /// Negative events *can* be recorded, but sum should not be filled out when
392    /// doing so.  This is specifically to enforce compatibility w/ OpenMetrics,
393    /// see: <https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md#histogram>
394    #[prost(double, optional, tag = "5")]
395    pub sum: ::core::option::Option<f64>,
396    /// bucket_counts is an optional field contains the count values of histogram
397    /// for each bucket.
398    ///
399    /// The sum of the bucket_counts must equal the value in the count field.
400    ///
401    /// The number of elements in bucket_counts array must be by one greater than
402    /// the number of elements in explicit_bounds array.
403    #[prost(fixed64, repeated, tag = "6")]
404    pub bucket_counts: ::prost::alloc::vec::Vec<u64>,
405    /// explicit_bounds specifies buckets with explicitly defined bounds for values.
406    ///
407    /// The boundaries for bucket at index i are:
408    ///
409    /// (-infinity, explicit_bounds\[i]\] for i == 0
410    /// (explicit_bounds\[i-1\], explicit_bounds\[i]\] for 0 < i < size(explicit_bounds)
411    /// (explicit_bounds\[i-1\], +infinity) for i == size(explicit_bounds)
412    ///
413    /// The values in the explicit_bounds array must be strictly increasing.
414    ///
415    /// Histogram buckets are inclusive of their upper boundary, except the last
416    /// bucket where the boundary is at infinity. This format is intentionally
417    /// compatible with the OpenMetrics histogram definition.
418    #[prost(double, repeated, tag = "7")]
419    pub explicit_bounds: ::prost::alloc::vec::Vec<f64>,
420    /// (Optional) List of exemplars collected from
421    /// measurements that were used to form the data point
422    #[prost(message, repeated, tag = "8")]
423    pub exemplars: ::prost::alloc::vec::Vec<Exemplar>,
424    /// Flags that apply to this specific data point.  See DataPointFlags
425    /// for the available flags and their meaning.
426    #[prost(uint32, tag = "10")]
427    pub flags: u32,
428    /// min is the minimum value over (start_time, end_time].
429    #[prost(double, optional, tag = "11")]
430    pub min: ::core::option::Option<f64>,
431    /// max is the maximum value over (start_time, end_time].
432    #[prost(double, optional, tag = "12")]
433    pub max: ::core::option::Option<f64>,
434}
435/// ExponentialHistogramDataPoint is a single data point in a timeseries that describes the
436/// time-varying values of a ExponentialHistogram of double values. A ExponentialHistogram contains
437/// summary statistics for a population of values, it may optionally contain the
438/// distribution of those values across a set of buckets.
439///
440#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
441#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
442#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
443#[allow(clippy::derive_partial_eq_without_eq)]
444#[derive(Clone, PartialEq, ::prost::Message)]
445pub struct ExponentialHistogramDataPoint {
446    /// The set of key/value pairs that uniquely identify the timeseries from
447    /// where this point belongs. The list may be empty (may contain 0 elements).
448    /// Attribute keys MUST be unique (it is not allowed to have more than one
449    /// attribute with the same key).
450    #[prost(message, repeated, tag = "1")]
451    pub attributes: ::prost::alloc::vec::Vec<super::super::common::v1::KeyValue>,
452    /// StartTimeUnixNano is optional but strongly encouraged, see the
453    /// the detailed comments above Metric.
454    ///
455    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
456    /// 1970.
457    #[prost(fixed64, tag = "2")]
458    pub start_time_unix_nano: u64,
459    /// TimeUnixNano is required, see the detailed comments above Metric.
460    ///
461    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
462    /// 1970.
463    #[prost(fixed64, tag = "3")]
464    pub time_unix_nano: u64,
465    /// count is the number of values in the population. Must be
466    /// non-negative. This value must be equal to the sum of the "bucket_counts"
467    /// values in the positive and negative Buckets plus the "zero_count" field.
468    #[prost(fixed64, tag = "4")]
469    pub count: u64,
470    /// sum of the values in the population. If count is zero then this field
471    /// must be zero.
472    ///
473    /// Note: Sum should only be filled out when measuring non-negative discrete
474    /// events, and is assumed to be monotonic over the values of these events.
475    /// Negative events *can* be recorded, but sum should not be filled out when
476    /// doing so.  This is specifically to enforce compatibility w/ OpenMetrics,
477    /// see: <https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md#histogram>
478    #[prost(double, optional, tag = "5")]
479    pub sum: ::core::option::Option<f64>,
480    /// scale describes the resolution of the histogram.  Boundaries are
481    /// located at powers of the base, where:
482    ///
483    ///    base = (2^(2^-scale))
484    ///
485    /// The histogram bucket identified by `index`, a signed integer,
486    /// contains values that are greater than (base^index) and
487    /// less than or equal to (base^(index+1)).
488    ///
489    /// The positive and negative ranges of the histogram are expressed
490    /// separately.  Negative values are mapped by their absolute value
491    /// into the negative range using the same scale as the positive range.
492    ///
493    /// scale is not restricted by the protocol, as the permissible
494    /// values depend on the range of the data.
495    #[prost(sint32, tag = "6")]
496    pub scale: i32,
497    /// zero_count is the count of values that are either exactly zero or
498    /// within the region considered zero by the instrumentation at the
499    /// tolerated degree of precision.  This bucket stores values that
500    /// cannot be expressed using the standard exponential formula as
501    /// well as values that have been rounded to zero.
502    ///
503    /// Implementations MAY consider the zero bucket to have probability
504    /// mass equal to (zero_count / count).
505    #[prost(fixed64, tag = "7")]
506    pub zero_count: u64,
507    /// positive carries the positive range of exponential bucket counts.
508    #[prost(message, optional, tag = "8")]
509    pub positive: ::core::option::Option<exponential_histogram_data_point::Buckets>,
510    /// negative carries the negative range of exponential bucket counts.
511    #[prost(message, optional, tag = "9")]
512    pub negative: ::core::option::Option<exponential_histogram_data_point::Buckets>,
513    /// Flags that apply to this specific data point.  See DataPointFlags
514    /// for the available flags and their meaning.
515    #[prost(uint32, tag = "10")]
516    pub flags: u32,
517    /// (Optional) List of exemplars collected from
518    /// measurements that were used to form the data point
519    #[prost(message, repeated, tag = "11")]
520    pub exemplars: ::prost::alloc::vec::Vec<Exemplar>,
521    /// min is the minimum value over (start_time, end_time].
522    #[prost(double, optional, tag = "12")]
523    pub min: ::core::option::Option<f64>,
524    /// max is the maximum value over (start_time, end_time].
525    #[prost(double, optional, tag = "13")]
526    pub max: ::core::option::Option<f64>,
527    /// ZeroThreshold may be optionally set to convey the width of the zero
528    /// region. Where the zero region is defined as the closed interval
529    /// \[-ZeroThreshold, ZeroThreshold\].
530    /// When ZeroThreshold is 0, zero count bucket stores values that cannot be
531    /// expressed using the standard exponential formula as well as values that
532    /// have been rounded to zero.
533    #[prost(double, tag = "14")]
534    pub zero_threshold: f64,
535}
536/// Nested message and enum types in `ExponentialHistogramDataPoint`.
537pub mod exponential_histogram_data_point {
538    /// Buckets are a set of bucket counts, encoded in a contiguous array
539    /// of counts.
540    #[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
541    #[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
542    #[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
543    #[allow(clippy::derive_partial_eq_without_eq)]
544    #[derive(Clone, PartialEq, ::prost::Message)]
545    pub struct Buckets {
546        /// Offset is the bucket index of the first entry in the bucket_counts array.
547        ///
548        /// Note: This uses a varint encoding as a simple form of compression.
549        #[prost(sint32, tag = "1")]
550        pub offset: i32,
551        /// bucket_counts is an array of count values, where bucket_counts\[i\] carries
552        /// the count of the bucket at index (offset+i). bucket_counts\[i\] is the count
553        /// of values greater than base^(offset+i) and less than or equal to
554        /// base^(offset+i+1).
555        ///
556        /// Note: By contrast, the explicit HistogramDataPoint uses
557        /// fixed64.  This field is expected to have many buckets,
558        /// especially zeros, so uint64 has been selected to ensure
559        /// varint encoding.
560        #[prost(uint64, repeated, tag = "2")]
561        pub bucket_counts: ::prost::alloc::vec::Vec<u64>,
562    }
563}
564/// SummaryDataPoint is a single data point in a timeseries that describes the
565/// time-varying values of a Summary metric.
566#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
567#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
568#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
569#[allow(clippy::derive_partial_eq_without_eq)]
570#[derive(Clone, PartialEq, ::prost::Message)]
571pub struct SummaryDataPoint {
572    /// The set of key/value pairs that uniquely identify the timeseries from
573    /// where this point belongs. The list may be empty (may contain 0 elements).
574    /// Attribute keys MUST be unique (it is not allowed to have more than one
575    /// attribute with the same key).
576    #[prost(message, repeated, tag = "7")]
577    pub attributes: ::prost::alloc::vec::Vec<super::super::common::v1::KeyValue>,
578    /// StartTimeUnixNano is optional but strongly encouraged, see the
579    /// the detailed comments above Metric.
580    ///
581    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
582    /// 1970.
583    #[prost(fixed64, tag = "2")]
584    pub start_time_unix_nano: u64,
585    /// TimeUnixNano is required, see the detailed comments above Metric.
586    ///
587    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
588    /// 1970.
589    #[prost(fixed64, tag = "3")]
590    pub time_unix_nano: u64,
591    /// count is the number of values in the population. Must be non-negative.
592    #[prost(fixed64, tag = "4")]
593    pub count: u64,
594    /// sum of the values in the population. If count is zero then this field
595    /// must be zero.
596    ///
597    /// Note: Sum should only be filled out when measuring non-negative discrete
598    /// events, and is assumed to be monotonic over the values of these events.
599    /// Negative events *can* be recorded, but sum should not be filled out when
600    /// doing so.  This is specifically to enforce compatibility w/ OpenMetrics,
601    /// see: <https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md#summary>
602    #[prost(double, tag = "5")]
603    pub sum: f64,
604    /// (Optional) list of values at different quantiles of the distribution calculated
605    /// from the current snapshot. The quantiles must be strictly increasing.
606    #[prost(message, repeated, tag = "6")]
607    pub quantile_values: ::prost::alloc::vec::Vec<summary_data_point::ValueAtQuantile>,
608    /// Flags that apply to this specific data point.  See DataPointFlags
609    /// for the available flags and their meaning.
610    #[prost(uint32, tag = "8")]
611    pub flags: u32,
612}
613/// Nested message and enum types in `SummaryDataPoint`.
614pub mod summary_data_point {
615    /// Represents the value at a given quantile of a distribution.
616    ///
617    /// To record Min and Max values following conventions are used:
618    /// - The 1.0 quantile is equivalent to the maximum value observed.
619    /// - The 0.0 quantile is equivalent to the minimum value observed.
620    ///
621    /// See the following issue for more context:
622    /// <https://github.com/open-telemetry/opentelemetry-proto/issues/125>
623    #[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
624    #[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
625    #[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
626    #[allow(clippy::derive_partial_eq_without_eq)]
627    #[derive(Clone, Copy, PartialEq, ::prost::Message)]
628    pub struct ValueAtQuantile {
629        /// The quantile of a distribution. Must be in the interval
630        /// \[0.0, 1.0\].
631        #[prost(double, tag = "1")]
632        pub quantile: f64,
633        /// The value at the given quantile of a distribution.
634        ///
635        /// Quantile values must NOT be negative.
636        #[prost(double, tag = "2")]
637        pub value: f64,
638    }
639}
640/// A representation of an exemplar, which is a sample input measurement.
641/// Exemplars also hold information about the environment when the measurement
642/// was recorded, for example the span and trace ID of the active span when the
643/// exemplar was recorded.
644#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
645#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
646#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
647#[allow(clippy::derive_partial_eq_without_eq)]
648#[derive(Clone, PartialEq, ::prost::Message)]
649pub struct Exemplar {
650    /// The set of key/value pairs that were filtered out by the aggregator, but
651    /// recorded alongside the original measurement. Only key/value pairs that were
652    /// filtered out by the aggregator should be included
653    #[prost(message, repeated, tag = "7")]
654    pub filtered_attributes: ::prost::alloc::vec::Vec<
655        super::super::common::v1::KeyValue,
656    >,
657    /// time_unix_nano is the exact time when this exemplar was recorded
658    ///
659    /// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
660    /// 1970.
661    #[prost(fixed64, tag = "2")]
662    pub time_unix_nano: u64,
663    /// (Optional) Span ID of the exemplar trace.
664    /// span_id may be missing if the measurement is not recorded inside a trace
665    /// or if the trace is not sampled.
666    #[prost(bytes = "vec", tag = "4")]
667    pub span_id: ::prost::alloc::vec::Vec<u8>,
668    /// (Optional) Trace ID of the exemplar trace.
669    /// trace_id may be missing if the measurement is not recorded inside a trace
670    /// or if the trace is not sampled.
671    #[prost(bytes = "vec", tag = "5")]
672    pub trace_id: ::prost::alloc::vec::Vec<u8>,
673    /// The value of the measurement that was recorded. An exemplar is
674    /// considered invalid when one of the recognized value fields is not present
675    /// inside this oneof.
676    #[prost(oneof = "exemplar::Value", tags = "3, 6")]
677    pub value: ::core::option::Option<exemplar::Value>,
678}
679/// Nested message and enum types in `Exemplar`.
680pub mod exemplar {
681    /// The value of the measurement that was recorded. An exemplar is
682    /// considered invalid when one of the recognized value fields is not present
683    /// inside this oneof.
684    #[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
685    #[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
686    #[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
687    #[allow(clippy::derive_partial_eq_without_eq)]
688    #[derive(Clone, Copy, PartialEq, ::prost::Oneof)]
689    pub enum Value {
690        #[prost(double, tag = "3")]
691        AsDouble(f64),
692        #[prost(sfixed64, tag = "6")]
693        AsInt(i64),
694    }
695}
696/// AggregationTemporality defines how a metric aggregator reports aggregated
697/// values. It describes how those values relate to the time interval over
698/// which they are aggregated.
699#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
700#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
701#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
702#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, ::prost::Enumeration)]
703#[repr(i32)]
704pub enum AggregationTemporality {
705    /// UNSPECIFIED is the default AggregationTemporality, it MUST not be used.
706    Unspecified = 0,
707    /// DELTA is an AggregationTemporality for a metric aggregator which reports
708    /// changes since last report time. Successive metrics contain aggregation of
709    /// values from continuous and non-overlapping intervals.
710    ///
711    /// The values for a DELTA metric are based only on the time interval
712    /// associated with one measurement cycle. There is no dependency on
713    /// previous measurements like is the case for CUMULATIVE metrics.
714    ///
715    /// For example, consider a system measuring the number of requests that
716    /// it receives and reports the sum of these requests every second as a
717    /// DELTA metric:
718    ///
719    ///    1. The system starts receiving at time=t_0.
720    ///    2. A request is received, the system measures 1 request.
721    ///    3. A request is received, the system measures 1 request.
722    ///    4. A request is received, the system measures 1 request.
723    ///    5. The 1 second collection cycle ends. A metric is exported for the
724    ///       number of requests received over the interval of time t_0 to
725    ///       t_0+1 with a value of 3.
726    ///    6. A request is received, the system measures 1 request.
727    ///    7. A request is received, the system measures 1 request.
728    ///    8. The 1 second collection cycle ends. A metric is exported for the
729    ///       number of requests received over the interval of time t_0+1 to
730    ///       t_0+2 with a value of 2.
731    Delta = 1,
732    /// CUMULATIVE is an AggregationTemporality for a metric aggregator which
733    /// reports changes since a fixed start time. This means that current values
734    /// of a CUMULATIVE metric depend on all previous measurements since the
735    /// start time. Because of this, the sender is required to retain this state
736    /// in some form. If this state is lost or invalidated, the CUMULATIVE metric
737    /// values MUST be reset and a new fixed start time following the last
738    /// reported measurement time sent MUST be used.
739    ///
740    /// For example, consider a system measuring the number of requests that
741    /// it receives and reports the sum of these requests every second as a
742    /// CUMULATIVE metric:
743    ///
744    ///    1. The system starts receiving at time=t_0.
745    ///    2. A request is received, the system measures 1 request.
746    ///    3. A request is received, the system measures 1 request.
747    ///    4. A request is received, the system measures 1 request.
748    ///    5. The 1 second collection cycle ends. A metric is exported for the
749    ///       number of requests received over the interval of time t_0 to
750    ///       t_0+1 with a value of 3.
751    ///    6. A request is received, the system measures 1 request.
752    ///    7. A request is received, the system measures 1 request.
753    ///    8. The 1 second collection cycle ends. A metric is exported for the
754    ///       number of requests received over the interval of time t_0 to
755    ///       t_0+2 with a value of 5.
756    ///    9. The system experiences a fault and loses state.
757    ///    10. The system recovers and resumes receiving at time=t_1.
758    ///    11. A request is received, the system measures 1 request.
759    ///    12. The 1 second collection cycle ends. A metric is exported for the
760    ///       number of requests received over the interval of time t_1 to
761    ///       t_0+1 with a value of 1.
762    ///
763    /// Note: Even though, when reporting changes since last report time, using
764    /// CUMULATIVE is valid, it is not recommended. This may cause problems for
765    /// systems that do not use start_time to determine when the aggregation
766    /// value was reset (e.g. Prometheus).
767    Cumulative = 2,
768}
769impl AggregationTemporality {
770    /// String value of the enum field names used in the ProtoBuf definition.
771    ///
772    /// The values are not transformed in any way and thus are considered stable
773    /// (if the ProtoBuf definition does not change) and safe for programmatic use.
774    pub fn as_str_name(&self) -> &'static str {
775        match self {
776            AggregationTemporality::Unspecified => "AGGREGATION_TEMPORALITY_UNSPECIFIED",
777            AggregationTemporality::Delta => "AGGREGATION_TEMPORALITY_DELTA",
778            AggregationTemporality::Cumulative => "AGGREGATION_TEMPORALITY_CUMULATIVE",
779        }
780    }
781    /// Creates an enum from field names used in the ProtoBuf definition.
782    pub fn from_str_name(value: &str) -> ::core::option::Option<Self> {
783        match value {
784            "AGGREGATION_TEMPORALITY_UNSPECIFIED" => Some(Self::Unspecified),
785            "AGGREGATION_TEMPORALITY_DELTA" => Some(Self::Delta),
786            "AGGREGATION_TEMPORALITY_CUMULATIVE" => Some(Self::Cumulative),
787            _ => None,
788        }
789    }
790}
791/// DataPointFlags is defined as a protobuf 'uint32' type and is to be used as a
792/// bit-field representing 32 distinct boolean flags.  Each flag defined in this
793/// enum is a bit-mask.  To test the presence of a single flag in the flags of
794/// a data point, for example, use an expression like:
795///
796///    (point.flags & DATA_POINT_FLAGS_NO_RECORDED_VALUE_MASK) == DATA_POINT_FLAGS_NO_RECORDED_VALUE_MASK
797///
798#[cfg_attr(feature = "with-schemars", derive(schemars::JsonSchema))]
799#[cfg_attr(feature = "with-serde", derive(serde::Serialize, serde::Deserialize))]
800#[cfg_attr(feature = "with-serde", serde(rename_all = "camelCase"))]
801#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, ::prost::Enumeration)]
802#[repr(i32)]
803pub enum DataPointFlags {
804    /// The zero value for the enum. Should not be used for comparisons.
805    /// Instead use bitwise "and" with the appropriate mask as shown above.
806    DoNotUse = 0,
807    /// This DataPoint is valid but has no recorded value.  This value
808    /// SHOULD be used to reflect explicitly missing data in a series, as
809    /// for an equivalent to the Prometheus "staleness marker".
810    NoRecordedValueMask = 1,
811}
812impl DataPointFlags {
813    /// String value of the enum field names used in the ProtoBuf definition.
814    ///
815    /// The values are not transformed in any way and thus are considered stable
816    /// (if the ProtoBuf definition does not change) and safe for programmatic use.
817    pub fn as_str_name(&self) -> &'static str {
818        match self {
819            DataPointFlags::DoNotUse => "DATA_POINT_FLAGS_DO_NOT_USE",
820            DataPointFlags::NoRecordedValueMask => {
821                "DATA_POINT_FLAGS_NO_RECORDED_VALUE_MASK"
822            }
823        }
824    }
825    /// Creates an enum from field names used in the ProtoBuf definition.
826    pub fn from_str_name(value: &str) -> ::core::option::Option<Self> {
827        match value {
828            "DATA_POINT_FLAGS_DO_NOT_USE" => Some(Self::DoNotUse),
829            "DATA_POINT_FLAGS_NO_RECORDED_VALUE_MASK" => Some(Self::NoRecordedValueMask),
830            _ => None,
831        }
832    }
833}
opentelemetry_proto/proto/tonic/opentelemetry.proto.metrics.v1.rs

opentelemetry_proto/proto/tonic/
opentelemetry.proto.metrics.v1.rs
