// Copyright 2019, OpenTelemetry Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

syntax = "proto3";

package opentelemetry;

import "lib/protoparser/opentelemetry/proto/common.proto";
import "lib/protoparser/opentelemetry/proto/resource.proto";

option csharp_namespace = "OpenTelemetry.Proto.Metrics.V1";
option java_multiple_files = true;
option java_package = "io.opentelemetry.proto.metrics.v1";
option java_outer_classname = "MetricsProto";
option go_package = "opentelemetry/pb";

// 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.
message MetricsData {
  // 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.
  repeated ResourceMetrics resource_metrics = 1;
}

// A collection of ScopeMetrics from a Resource.
message ResourceMetrics {
  reserved 1000;

  // The resource for the metrics in this message.
  // If this field is not set then no resource info is known.
  Resource resource = 1;

  // A list of metrics that originate from a resource.
  repeated ScopeMetrics scope_metrics = 2;

  // 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.
  string schema_url = 3;
}

// A collection of Metrics produced by an Scope.
message ScopeMetrics {
  // A list of metrics that originate from an instrumentation library.
  repeated Metric metrics = 2;

  // This schema_url applies to all metrics in the "metrics" field.
  string schema_url = 3;
}

// 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/data-model.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.
message Metric {
  reserved 4, 6, 8;

  // name of the metric, including its DNS name prefix. It must be unique.
  string name = 1;

  // description of the metric, which can be used in documentation.
  string description = 2;

  // unit in which the metric value is reported. Follows the format
  // described by http://unitsofmeasure.org/ucum.html.
  string unit = 3;

  // Data determines the aggregation type (if any) of the metric, what is the
  // reported value type for the data points, as well as the relatationship to
  // the time interval over which they are reported.
  oneof data {
    Gauge gauge = 5;
    Sum sum = 7;
    Histogram histogram = 9;
    ExponentialHistogram exponential_histogram = 10;
    Summary summary = 11;
  }
}

// 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.
message Gauge {
  repeated NumberDataPoint data_points = 1;
}

// Sum represents the type of a scalar metric that is calculated as a sum of all
// reported measurements over a time interval.
message Sum {
  repeated NumberDataPoint data_points = 1;

  // aggregation_temporality describes if the aggregator reports delta changes
  // since last report time, or cumulative changes since a fixed start time.
  AggregationTemporality aggregation_temporality = 2;

  // If "true" means that the sum is monotonic.
  bool is_monotonic = 3;
}

// Histogram represents the type of a metric that is calculated by aggregating
// as a Histogram of all reported measurements over a time interval.
message Histogram {
  repeated HistogramDataPoint data_points = 1;

  // aggregation_temporality describes if the aggregator reports delta changes
  // since last report time, or cumulative changes since a fixed start time.
  AggregationTemporality aggregation_temporality = 2;
}

// ExponentialHistogram represents the type of a metric that is calculated by aggregating
// as a ExponentialHistogram of all reported double measurements over a time interval.
message ExponentialHistogram {
  repeated ExponentialHistogramDataPoint data_points = 1;

  // aggregation_temporality describes if the aggregator reports delta changes
  // since last report time, or cumulative changes since a fixed start time.
  AggregationTemporality aggregation_temporality = 2;
}

// 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.
message Summary {
  repeated SummaryDataPoint data_points = 1;
}

// AggregationTemporality defines how a metric aggregator reports aggregated
// values. It describes how those values relate to the time interval over
// which they are aggregated.
enum AggregationTemporality {
  // UNSPECIFIED is the default AggregationTemporality, it MUST not be used.
  AGGREGATION_TEMPORALITY_UNSPECIFIED = 0;

  // DELTA is an AggregationTemporality for a metric aggregator which reports
  // changes since last report time. Successive metrics contain aggregation of
  // values from continuous and non-overlapping intervals.
  //
  // The values for a DELTA metric are based only on the time interval
  // associated with one measurement cycle. There is no dependency on
  // previous measurements like is the case for CUMULATIVE metrics.
  //
  // For example, consider a system measuring the number of requests that
  // it receives and reports the sum of these requests every second as a
  // DELTA metric:
  //
  //   1. The system starts receiving at time=t_0.
  //   2. A request is received, the system measures 1 request.
  //   3. A request is received, the system measures 1 request.
  //   4. A request is received, the system measures 1 request.
  //   5. The 1 second collection cycle ends. A metric is exported for the
  //      number of requests received over the interval of time t_0 to
  //      t_0+1 with a value of 3.
  //   6. A request is received, the system measures 1 request.
  //   7. A request is received, the system measures 1 request.
  //   8. The 1 second collection cycle ends. A metric is exported for the
  //      number of requests received over the interval of time t_0+1 to
  //      t_0+2 with a value of 2.
  AGGREGATION_TEMPORALITY_DELTA = 1;

  // CUMULATIVE is an AggregationTemporality for a metric aggregator which
  // reports changes since a fixed start time. This means that current values
  // of a CUMULATIVE metric depend on all previous measurements since the
  // start time. Because of this, the sender is required to retain this state
  // in some form. If this state is lost or invalidated, the CUMULATIVE metric
  // values MUST be reset and a new fixed start time following the last
  // reported measurement time sent MUST be used.
  //
  // For example, consider a system measuring the number of requests that
  // it receives and reports the sum of these requests every second as a
  // CUMULATIVE metric:
  //
  //   1. The system starts receiving at time=t_0.
  //   2. A request is received, the system measures 1 request.
  //   3. A request is received, the system measures 1 request.
  //   4. A request is received, the system measures 1 request.
  //   5. The 1 second collection cycle ends. A metric is exported for the
  //      number of requests received over the interval of time t_0 to
  //      t_0+1 with a value of 3.
  //   6. A request is received, the system measures 1 request.
  //   7. A request is received, the system measures 1 request.
  //   8. The 1 second collection cycle ends. A metric is exported for the
  //      number of requests received over the interval of time t_0 to
  //      t_0+2 with a value of 5.
  //   9. The system experiences a fault and loses state.
  //   10. The system recovers and resumes receiving at time=t_1.
  //   11. A request is received, the system measures 1 request.
  //   12. The 1 second collection cycle ends. A metric is exported for the
  //      number of requests received over the interval of time t_1 to
  //      t_0+1 with a value of 1.
  //
  // Note: Even though, when reporting changes since last report time, using
  // CUMULATIVE is valid, it is not recommended. This may cause problems for
  // systems that do not use start_time to determine when the aggregation
  // value was reset (e.g. Prometheus).
  AGGREGATION_TEMPORALITY_CUMULATIVE = 2;
}

// 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
//
enum DataPointFlags {
  FLAG_NONE = 0;

  // This DataPoint is valid but has no recorded value.  This value
  // SHOULD be used to reflect explicitly missing data in a series, as
  // for an equivalent to the Prometheus "staleness marker".
  FLAG_NO_RECORDED_VALUE = 1;

  // Bits 2-31 are reserved for future use.
}

// NumberDataPoint is a single data point in a timeseries that describes the
// time-varying scalar value of a metric.
message NumberDataPoint {
  reserved 1;

  // 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).
  repeated KeyValue attributes = 7;

  // 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.
  fixed64 start_time_unix_nano = 2;

  // 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.
  fixed64 time_unix_nano = 3;

  // The value itself.  A point is considered invalid when one of the recognized
  // value fields is not present inside this oneof.
  oneof value {
    double as_double = 4;
    sfixed64 as_int = 6;
  }

  // (Optional) List of exemplars collected from
  // measurements that were used to form the data point
  repeated Exemplar exemplars = 5;

  // Flags that apply to this specific data point.  See DataPointFlags
  // for the available flags and their meaning.
  uint32 flags = 8;
}

// 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.
message HistogramDataPoint {
  reserved 1;

  // 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).
  repeated KeyValue attributes = 9;

  // 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.
  fixed64 start_time_unix_nano = 2;

  // 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.
  fixed64 time_unix_nano = 3;

  // 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.
  fixed64 count = 4;

  // 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
  optional double sum = 5;

  // 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.
  repeated fixed64 bucket_counts = 6;

  // 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.
  repeated double explicit_bounds = 7;

  // (Optional) List of exemplars collected from
  // measurements that were used to form the data point
  repeated Exemplar exemplars = 8;

  // Flags that apply to this specific data point.  See DataPointFlags
  // for the available flags and their meaning.
  uint32 flags = 10;

  // min is the minimum value over (start_time, end_time].
  optional double min = 11;

  // max is the maximum value over (start_time, end_time].
  optional double max = 12;
}

// 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.
//
message ExponentialHistogramDataPoint {
  // 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).
  repeated KeyValue attributes = 1;

  // 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.
  fixed64 start_time_unix_nano = 2;

  // 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.
  fixed64 time_unix_nano = 3;

  // 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.
  fixed64 count = 4;

  // 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
  optional double sum = 5;
  
  // 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 (base^index) and
  // less than or equal to (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.
  sint32 scale = 6;

  // 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).
  fixed64 zero_count = 7;

  // positive carries the positive range of exponential bucket counts.
  Buckets positive = 8;

  // negative carries the negative range of exponential bucket counts.
  Buckets negative = 9;

  // Buckets are a set of bucket counts, encoded in a contiguous array
  // of counts.
  message Buckets {
    // 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.
    sint32 offset = 1;

    // 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 base^(offset+i) and less or equal to 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.
    repeated uint64 bucket_counts = 2;
  } 

  // Flags that apply to this specific data point.  See DataPointFlags
  // for the available flags and their meaning.
  uint32 flags = 10;

  // (Optional) List of exemplars collected from
  // measurements that were used to form the data point
  repeated Exemplar exemplars = 11;

  // min is the minimum value over (start_time, end_time].
  optional double min = 12;

  // max is the maximum value over (start_time, end_time].
  optional double max = 13;
}

// SummaryDataPoint is a single data point in a timeseries that describes the
// time-varying values of a Summary metric.
message SummaryDataPoint {
  reserved 1;

  // 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).
  repeated KeyValue attributes = 7;

  // 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.
  fixed64 start_time_unix_nano = 2;

  // 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.
  fixed64 time_unix_nano = 3;

  // count is the number of values in the population. Must be non-negative.
  fixed64 count = 4;

  // 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
  double sum = 5;

  // Represents the value at a given quantile of a distribution.
  //
  // To record Min and Max values following conventions are used:
  // - The 1.0 quantile is equivalent to the maximum value observed.
  // - The 0.0 quantile is equivalent to the minimum value observed.
  //
  // See the following issue for more context:
  // https://github.com/open-telemetry/opentelemetry-proto/issues/125
  message ValueAtQuantile {
    // The quantile of a distribution. Must be in the interval
    // [0.0, 1.0].
    double quantile = 1;

    // The value at the given quantile of a distribution.
    //
    // Quantile values must NOT be negative.
    double value = 2;
  }

  // (Optional) list of values at different quantiles of the distribution calculated
  // from the current snapshot. The quantiles must be strictly increasing.
  repeated ValueAtQuantile quantile_values = 6;

  // Flags that apply to this specific data point.  See DataPointFlags
  // for the available flags and their meaning.
  uint32 flags = 8;
}

// 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.
message Exemplar {
  reserved 1;

  // 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
  repeated KeyValue filtered_attributes = 7;

  // 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.
  fixed64 time_unix_nano = 2;

  // The value of the measurement that was recorded. An exemplar is
  // considered invalid when one of the recognized value fields is not present
  // inside this oneof.
  oneof value {
    double as_double = 3;
    sfixed64 as_int = 6;
  }

  // (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.
  bytes span_id = 4;

  // (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.
  bytes trace_id = 5;
}