enum AggType {
    Gauge
    Sum
    Histogram
    ExponentialHistogram
    Summary
}
# Defines a Metric which has one or more timeseries
# 
#  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.
#
type Metrics @model{
    name:String
    description:String
    # unit in which the metric value is reported. Follows the format
    # described by http://unitsofmeasure.org/ucum.html.
    unit:String
    # 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.
    data:MetricsData
}

interface MetricsData {
    points:[DataPoint]
    type:AggType
}

#  List of exemplars collected from measurements that were used to form the data point
type Exemplar {
    filteredAttributes:JSON
    value:DataPointValue
    time:Time
    spanId:String
    traceId:String
}

interface DataPoint {
    #    Attributes includes key-value pairs associated with the data point (AKA dimensions)
    attributes:JSON
    #    time field is set to the end time of the aggregation
    time:Time
    # 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.
    startTime:Time

}

# 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 {

    # 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

    # 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

}

# The actual value itself 
type DataPointValue {
    valueAsDouble:Float
    valueAsInt:Int
}

type QuantileValue {
    #   The quantile of a distribution. Must be in the interval [0.0, 1.0].
    quantile:Float
    #     The value at the given quantile of a distribution.
    value:Float
}

# NumberDataPoint is a single data point in a timeseries that describes the time-varying scalar value of a metric.
type NumberDataPoint implements DataPoint {
    #    Attributes includes key-value pairs associated with the data point (AKA dimensions)
    attributes:JSON
    #    time field is set to the end time of the aggregation
    time:Time
    # 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.
    startTime:Time

    value:DataPointValue
    # (Optional) List of exemplars collected from
    # measurements that were used to form the data point
    exemplar:Exemplar
}

# SummaryDataPoint is a single data point in a timeseries that describes the
# time-varying values of a Summary metric.
type SummaryDataPoint implements DataPoint {
    #    Attributes includes key-value pairs associated with the data point (AKA dimensions)
    attributes:JSON
    #    time field is set to the end time of the aggregation
    time:Time
    # 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.
    startTime:Time

    # sum of the values in the population. If count is zero then this field must be zero.
    count:Long
    # sum of the values in the population. If count is zero then this field must be zero.
    sum : Long
    # (Optional) list of values at different quantiles of the distribution calculated
    # from the current snapshot. The quantiles must be strictly increasing.
    quantileValues:[QuantileValue]
}

#  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.
type HistogramDataPoint implements DataPoint {
    #    Attributes includes key-value pairs associated with the data point (AKA dimensions)
    attributes:JSON
    #    time field is set to the end time of the aggregation
    time:Time
    # 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.
    startTime:Time

    # sum of the values in the population. If count is zero then this field must be zero.
    count:Long
    #    bucket_counts is an optional field contains the count values of histogram for each bucket.
    bucketCounts:[Int]
    #  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. 
    bucketBounds:[Float]

    #  sum of the values in the population. If count is zero then this field
    #  must be zero.
    #
    sum : Long

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

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

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

}

#  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.
type ExponentialHistogramDataPoint implements DataPoint {
    #    Attributes includes key-value pairs associated with the data point (AKA dimensions)
    attributes:JSON
    #    time field is set to the end time of the aggregation
    time:Time
    # 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.
    startTime:Time

    # TODO - implement content
}

#  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.
type Gauge implements MetricsData{
    points:[NumberDataPoint]
    type:AggType
}

# Sum represents the type of a scalar metric that is calculated as a sum of all reported measurements over a time interval.
type Sum implements MetricsData{
    points:[SummaryDataPoint]
    type:AggType

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

}

#  Summary metric data are used to convey quantile summaries 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.
type Summary implements MetricsData{
    points:[SummaryDataPoint]
    type:AggType

    # aggregation_temporality describes if the aggregator reports delta changes
    # since last report time, or cumulative changes since a fixed start time.
    temporality:AggregationTemporality
}

# Histogram represents the type of a metric that is calculated by aggregating
# as a Histogram of all reported measurements over a time interval.
type Histogram implements MetricsData{
    points:[HistogramDataPoint]
    type:AggType

    # aggregation_temporality describes if the aggregator reports delta changes
    # since last report time, or cumulative changes since a fixed start time.
    temporality:AggregationTemporality
}

# ExponentialHistogram represents the type of a metric that is calculated by aggregating
# as a ExponentialHistogram of all reported double measurements over a time interval.
type ExponentialHistogram implements MetricsData{
    points:[ExponentialHistogramDataPoint]
    type:AggType

    # aggregation_temporality describes if the aggregator reports delta changes
    # since last report time, or cumulative changes since a fixed start time.
    temporality:AggregationTemporality
}