// Copyright 2021 Google LLC
//
// 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 google.monitoring.v3;

import "google/api/distribution.proto";
import "google/protobuf/duration.proto";
import "google/protobuf/timestamp.proto";

option csharp_namespace = "Google.Cloud.Monitoring.V3";
option go_package = "cloud.google.com/go/monitoring/apiv3/v2/monitoringpb;monitoringpb";
option java_multiple_files = true;
option java_outer_classname = "CommonProto";
option java_package = "com.google.monitoring.v3";
option php_namespace = "Google\\Cloud\\Monitoring\\V3";
option ruby_package = "Google::Cloud::Monitoring::V3";

// A single strongly-typed value.
message TypedValue {
  // The typed value field.
  oneof value {
    // A Boolean value: `true` or `false`.
    bool bool_value = 1;

    // A 64-bit integer. Its range is approximately &plusmn;9.2x10<sup>18</sup>.
    int64 int64_value = 2;

    // A 64-bit double-precision floating-point number. Its magnitude
    // is approximately &plusmn;10<sup>&plusmn;300</sup> and it has 16
    // significant digits of precision.
    double double_value = 3;

    // A variable-length string value.
    string string_value = 4;

    // A distribution value.
    google.api.Distribution distribution_value = 5;
  }
}

// A closed time interval. It extends from the start time to the end time, and includes both: `[startTime, endTime]`. Valid time intervals depend on the [`MetricKind`](https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors#MetricKind) of the metric value. The end time must not be earlier than the start time. When writing data points, the start time must not be more than 25 hours in the past and the end time must not be more than five minutes in the future.
//
// * For `GAUGE` metrics, the `startTime` value is technically optional; if
//   no value is specified, the start time defaults to the value of the
//   end time, and the interval represents a single point in time. If both
//   start and end times are specified, they must be identical. Such an
//   interval is valid only for `GAUGE` metrics, which are point-in-time
//   measurements. The end time of a new interval must be at least a
//   millisecond after the end time of the previous interval.
//
// * For `DELTA` metrics, the start time and end time must specify a
//   non-zero interval, with subsequent points specifying contiguous and
//   non-overlapping intervals. For `DELTA` metrics, the start time of
//   the next interval must be at least a millisecond after the end time
//   of the previous interval.
//
// * For `CUMULATIVE` metrics, the start time and end time must specify a
//   non-zero interval, with subsequent points specifying the same
//   start time and increasing end times, until an event resets the
//   cumulative value to zero and sets a new start time for the following
//   points. The new start time must be at least a millisecond after the
//   end time of the previous interval.
//
// * The start time of a new interval must be at least a millisecond after the
//   end time of the previous interval because intervals are closed. If the
//   start time of a new interval is the same as the end time of the previous
//   interval, then data written at the new start time could overwrite data
//   written at the previous end time.
message TimeInterval {
  // Required. The end of the time interval.
  google.protobuf.Timestamp end_time = 2;

  // Optional. The beginning of the time interval.  The default value
  // for the start time is the end time. The start time must not be
  // later than the end time.
  google.protobuf.Timestamp start_time = 1;
}

// Describes how to combine multiple time series to provide a different view of
// the data.  Aggregation of time series is done in two steps. First, each time
// series in the set is _aligned_ to the same time interval boundaries, then the
// set of time series is optionally _reduced_ in number.
//
// Alignment consists of applying the `per_series_aligner` operation
// to each time series after its data has been divided into regular
// `alignment_period` time intervals. This process takes _all_ of the data
// points in an alignment period, applies a mathematical transformation such as
// averaging, minimum, maximum, delta, etc., and converts them into a single
// data point per period.
//
// Reduction is when the aligned and transformed time series can optionally be
// combined, reducing the number of time series through similar mathematical
// transformations. Reduction involves applying a `cross_series_reducer` to
// all the time series, optionally sorting the time series into subsets with
// `group_by_fields`, and applying the reducer to each subset.
//
// The raw time series data can contain a huge amount of information from
// multiple sources. Alignment and reduction transforms this mass of data into
// a more manageable and representative collection of data, for example "the
// 95% latency across the average of all tasks in a cluster". This
// representative data can be more easily graphed and comprehended, and the
// individual time series data is still available for later drilldown. For more
// details, see [Filtering and
// aggregation](https://cloud.google.com/monitoring/api/v3/aggregation).
message Aggregation {
  // The `Aligner` specifies the operation that will be applied to the data
  // points in each alignment period in a time series. Except for
  // `ALIGN_NONE`, which specifies that no operation be applied, each alignment
  // operation replaces the set of data values in each alignment period with
  // a single value: the result of applying the operation to the data values.
  // An aligned time series has a single data value at the end of each
  // `alignment_period`.
  //
  // An alignment operation can change the data type of the values, too. For
  // example, if you apply a counting operation to boolean values, the data
  // `value_type` in the original time series is `BOOLEAN`, but the `value_type`
  // in the aligned result is `INT64`.
  enum Aligner {
    // No alignment. Raw data is returned. Not valid if cross-series reduction
    // is requested. The `value_type` of the result is the same as the
    // `value_type` of the input.
    ALIGN_NONE = 0;

    // Align and convert to
    // [DELTA][google.api.MetricDescriptor.MetricKind.DELTA].
    // The output is `delta = y1 - y0`.
    //
    // This alignment is valid for
    // [CUMULATIVE][google.api.MetricDescriptor.MetricKind.CUMULATIVE] and
    // `DELTA` metrics. If the selected alignment period results in periods
    // with no data, then the aligned value for such a period is created by
    // interpolation. The `value_type`  of the aligned result is the same as
    // the `value_type` of the input.
    ALIGN_DELTA = 1;

    // Align and convert to a rate. The result is computed as
    // `rate = (y1 - y0)/(t1 - t0)`, or "delta over time".
    // Think of this aligner as providing the slope of the line that passes
    // through the value at the start and at the end of the `alignment_period`.
    //
    // This aligner is valid for `CUMULATIVE`
    // and `DELTA` metrics with numeric values. If the selected alignment
    // period results in periods with no data, then the aligned value for
    // such a period is created by interpolation. The output is a `GAUGE`
    // metric with `value_type` `DOUBLE`.
    //
    // If, by "rate", you mean "percentage change", see the
    // `ALIGN_PERCENT_CHANGE` aligner instead.
    ALIGN_RATE = 2;

    // Align by interpolating between adjacent points around the alignment
    // period boundary. This aligner is valid for `GAUGE` metrics with
    // numeric values. The `value_type` of the aligned result is the same as the
    // `value_type` of the input.
    ALIGN_INTERPOLATE = 3;

    // Align by moving the most recent data point before the end of the
    // alignment period to the boundary at the end of the alignment
    // period. This aligner is valid for `GAUGE` metrics. The `value_type` of
    // the aligned result is the same as the `value_type` of the input.
    ALIGN_NEXT_OLDER = 4;

    // Align the time series by returning the minimum value in each alignment
    // period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    // numeric values. The `value_type` of the aligned result is the same as
    // the `value_type` of the input.
    ALIGN_MIN = 10;

    // Align the time series by returning the maximum value in each alignment
    // period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    // numeric values. The `value_type` of the aligned result is the same as
    // the `value_type` of the input.
    ALIGN_MAX = 11;

    // Align the time series by returning the mean value in each alignment
    // period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    // numeric values. The `value_type` of the aligned result is `DOUBLE`.
    ALIGN_MEAN = 12;

    // Align the time series by returning the number of values in each alignment
    // period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    // numeric or Boolean values. The `value_type` of the aligned result is
    // `INT64`.
    ALIGN_COUNT = 13;

    // Align the time series by returning the sum of the values in each
    // alignment period. This aligner is valid for `GAUGE` and `DELTA`
    // metrics with numeric and distribution values. The `value_type` of the
    // aligned result is the same as the `value_type` of the input.
    ALIGN_SUM = 14;

    // Align the time series by returning the standard deviation of the values
    // in each alignment period. This aligner is valid for `GAUGE` and
    // `DELTA` metrics with numeric values. The `value_type` of the output is
    // `DOUBLE`.
    ALIGN_STDDEV = 15;

    // Align the time series by returning the number of `True` values in
    // each alignment period. This aligner is valid for `GAUGE` metrics with
    // Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_TRUE = 16;

    // Align the time series by returning the number of `False` values in
    // each alignment period. This aligner is valid for `GAUGE` metrics with
    // Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_FALSE = 24;

    // Align the time series by returning the ratio of the number of `True`
    // values to the total number of values in each alignment period. This
    // aligner is valid for `GAUGE` metrics with Boolean values. The output
    // value is in the range [0.0, 1.0] and has `value_type` `DOUBLE`.
    ALIGN_FRACTION_TRUE = 17;

    // Align the time series by using [percentile
    // aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    // data point in each alignment period is the 99th percentile of all data
    // points in the period. This aligner is valid for `GAUGE` and `DELTA`
    // metrics with distribution values. The output is a `GAUGE` metric with
    // `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_99 = 18;

    // Align the time series by using [percentile
    // aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    // data point in each alignment period is the 95th percentile of all data
    // points in the period. This aligner is valid for `GAUGE` and `DELTA`
    // metrics with distribution values. The output is a `GAUGE` metric with
    // `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_95 = 19;

    // Align the time series by using [percentile
    // aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    // data point in each alignment period is the 50th percentile of all data
    // points in the period. This aligner is valid for `GAUGE` and `DELTA`
    // metrics with distribution values. The output is a `GAUGE` metric with
    // `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_50 = 20;

    // Align the time series by using [percentile
    // aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    // data point in each alignment period is the 5th percentile of all data
    // points in the period. This aligner is valid for `GAUGE` and `DELTA`
    // metrics with distribution values. The output is a `GAUGE` metric with
    // `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_05 = 21;

    // Align and convert to a percentage change. This aligner is valid for
    // `GAUGE` and `DELTA` metrics with numeric values. This alignment returns
    // `((current - previous)/previous) * 100`, where the value of `previous` is
    // determined based on the `alignment_period`.
    //
    // If the values of `current` and `previous` are both 0, then the returned
    // value is 0. If only `previous` is 0, the returned value is infinity.
    //
    // A 10-minute moving mean is computed at each point of the alignment period
    // prior to the above calculation to smooth the metric and prevent false
    // positives from very short-lived spikes. The moving mean is only
    // applicable for data whose values are `>= 0`. Any values `< 0` are
    // treated as a missing datapoint, and are ignored. While `DELTA`
    // metrics are accepted by this alignment, special care should be taken that
    // the values for the metric will always be positive. The output is a
    // `GAUGE` metric with `value_type` `DOUBLE`.
    ALIGN_PERCENT_CHANGE = 23;
  }

  // A Reducer operation describes how to aggregate data points from multiple
  // time series into a single time series, where the value of each data point
  // in the resulting series is a function of all the already aligned values in
  // the input time series.
  enum Reducer {
    // No cross-time series reduction. The output of the `Aligner` is
    // returned.
    REDUCE_NONE = 0;

    // Reduce by computing the mean value across time series for each
    // alignment period. This reducer is valid for
    // [DELTA][google.api.MetricDescriptor.MetricKind.DELTA] and
    // [GAUGE][google.api.MetricDescriptor.MetricKind.GAUGE] metrics with
    // numeric or distribution values. The `value_type` of the output is
    // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
    REDUCE_MEAN = 1;

    // Reduce by computing the minimum value across time series for each
    // alignment period. This reducer is valid for `DELTA` and `GAUGE` metrics
    // with numeric values. The `value_type` of the output is the same as the
    // `value_type` of the input.
    REDUCE_MIN = 2;

    // Reduce by computing the maximum value across time series for each
    // alignment period. This reducer is valid for `DELTA` and `GAUGE` metrics
    // with numeric values. The `value_type` of the output is the same as the
    // `value_type` of the input.
    REDUCE_MAX = 3;

    // Reduce by computing the sum across time series for each
    // alignment period. This reducer is valid for `DELTA` and `GAUGE` metrics
    // with numeric and distribution values. The `value_type` of the output is
    // the same as the `value_type` of the input.
    REDUCE_SUM = 4;

    // Reduce by computing the standard deviation across time series
    // for each alignment period. This reducer is valid for `DELTA` and
    // `GAUGE` metrics with numeric or distribution values. The `value_type`
    // of the output is `DOUBLE`.
    REDUCE_STDDEV = 5;

    // Reduce by computing the number of data points across time series
    // for each alignment period. This reducer is valid for `DELTA` and
    // `GAUGE` metrics of numeric, Boolean, distribution, and string
    // `value_type`. The `value_type` of the output is `INT64`.
    REDUCE_COUNT = 6;

    // Reduce by computing the number of `True`-valued data points across time
    // series for each alignment period. This reducer is valid for `DELTA` and
    // `GAUGE` metrics of Boolean `value_type`. The `value_type` of the output
    // is `INT64`.
    REDUCE_COUNT_TRUE = 7;

    // Reduce by computing the number of `False`-valued data points across time
    // series for each alignment period. This reducer is valid for `DELTA` and
    // `GAUGE` metrics of Boolean `value_type`. The `value_type` of the output
    // is `INT64`.
    REDUCE_COUNT_FALSE = 15;

    // Reduce by computing the ratio of the number of `True`-valued data points
    // to the total number of data points for each alignment period. This
    // reducer is valid for `DELTA` and `GAUGE` metrics of Boolean `value_type`.
    // The output value is in the range [0.0, 1.0] and has `value_type`
    // `DOUBLE`.
    REDUCE_FRACTION_TRUE = 8;

    // Reduce by computing the [99th
    // percentile](https://en.wikipedia.org/wiki/Percentile) of data points
    // across time series for each alignment period. This reducer is valid for
    // `GAUGE` and `DELTA` metrics of numeric and distribution type. The value
    // of the output is `DOUBLE`.
    REDUCE_PERCENTILE_99 = 9;

    // Reduce by computing the [95th
    // percentile](https://en.wikipedia.org/wiki/Percentile) of data points
    // across time series for each alignment period. This reducer is valid for
    // `GAUGE` and `DELTA` metrics of numeric and distribution type. The value
    // of the output is `DOUBLE`.
    REDUCE_PERCENTILE_95 = 10;

    // Reduce by computing the [50th
    // percentile](https://en.wikipedia.org/wiki/Percentile) of data points
    // across time series for each alignment period. This reducer is valid for
    // `GAUGE` and `DELTA` metrics of numeric and distribution type. The value
    // of the output is `DOUBLE`.
    REDUCE_PERCENTILE_50 = 11;

    // Reduce by computing the [5th
    // percentile](https://en.wikipedia.org/wiki/Percentile) of data points
    // across time series for each alignment period. This reducer is valid for
    // `GAUGE` and `DELTA` metrics of numeric and distribution type. The value
    // of the output is `DOUBLE`.
    REDUCE_PERCENTILE_05 = 12;
  }

  // The `alignment_period` specifies a time interval, in seconds, that is used
  // to divide the data in all the
  // [time series][google.monitoring.v3.TimeSeries] into consistent blocks of
  // time. This will be done before the per-series aligner can be applied to
  // the data.
  //
  // The value must be at least 60 seconds. If a per-series
  // aligner other than `ALIGN_NONE` is specified, this field is required or an
  // error is returned. If no per-series aligner is specified, or the aligner
  // `ALIGN_NONE` is specified, then this field is ignored.
  //
  // The maximum value of the `alignment_period` is 104 weeks (2 years) for
  // charts, and 90,000 seconds (25 hours) for alerting policies.
  google.protobuf.Duration alignment_period = 1;

  // An `Aligner` describes how to bring the data points in a single
  // time series into temporal alignment. Except for `ALIGN_NONE`, all
  // alignments cause all the data points in an `alignment_period` to be
  // mathematically grouped together, resulting in a single data point for
  // each `alignment_period` with end timestamp at the end of the period.
  //
  // Not all alignment operations may be applied to all time series. The valid
  // choices depend on the `metric_kind` and `value_type` of the original time
  // series. Alignment can change the `metric_kind` or the `value_type` of
  // the time series.
  //
  // Time series data must be aligned in order to perform cross-time
  // series reduction. If `cross_series_reducer` is specified, then
  // `per_series_aligner` must be specified and not equal to `ALIGN_NONE`
  // and `alignment_period` must be specified; otherwise, an error is
  // returned.
  Aligner per_series_aligner = 2;

  // The reduction operation to be used to combine time series into a single
  // time series, where the value of each data point in the resulting series is
  // a function of all the already aligned values in the input time series.
  //
  // Not all reducer operations can be applied to all time series. The valid
  // choices depend on the `metric_kind` and the `value_type` of the original
  // time series. Reduction can yield a time series with a different
  // `metric_kind` or `value_type` than the input time series.
  //
  // Time series data must first be aligned (see `per_series_aligner`) in order
  // to perform cross-time series reduction. If `cross_series_reducer` is
  // specified, then `per_series_aligner` must be specified, and must not be
  // `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an
  // error is returned.
  Reducer cross_series_reducer = 4;

  // The set of fields to preserve when `cross_series_reducer` is
  // specified. The `group_by_fields` determine how the time series are
  // partitioned into subsets prior to applying the aggregation
  // operation. Each subset contains time series that have the same
  // value for each of the grouping fields. Each individual time
  // series is a member of exactly one subset. The
  // `cross_series_reducer` is applied to each subset of time series.
  // It is not possible to reduce across different resource types, so
  // this field implicitly contains `resource.type`.  Fields not
  // specified in `group_by_fields` are aggregated away.  If
  // `group_by_fields` is not specified and all the time series have
  // the same resource type, then the time series are aggregated into
  // a single output time series. If `cross_series_reducer` is not
  // defined, this field is ignored.
  repeated string group_by_fields = 5;
}

// Specifies an ordering relationship on two arguments, called `left` and
// `right`.
enum ComparisonType {
  // No ordering relationship is specified.
  COMPARISON_UNSPECIFIED = 0;

  // True if the left argument is greater than the right argument.
  COMPARISON_GT = 1;

  // True if the left argument is greater than or equal to the right argument.
  COMPARISON_GE = 2;

  // True if the left argument is less than the right argument.
  COMPARISON_LT = 3;

  // True if the left argument is less than or equal to the right argument.
  COMPARISON_LE = 4;

  // True if the left argument is equal to the right argument.
  COMPARISON_EQ = 5;

  // True if the left argument is not equal to the right argument.
  COMPARISON_NE = 6;
}

// The tier of service for a Workspace. Please see the
// [service tiers
// documentation](https://cloud.google.com/monitoring/workspaces/tiers) for more
// details.
enum ServiceTier {
  option deprecated = true;

  // An invalid sentinel value, used to indicate that a tier has not
  // been provided explicitly.
  SERVICE_TIER_UNSPECIFIED = 0;

  // The Stackdriver Basic tier, a free tier of service that provides basic
  // features, a moderate allotment of logs, and access to built-in metrics.
  // A number of features are not available in this tier. For more details,
  // see [the service tiers
  // documentation](https://cloud.google.com/monitoring/workspaces/tiers).
  SERVICE_TIER_BASIC = 1;

  // The Stackdriver Premium tier, a higher, more expensive tier of service
  // that provides access to all Stackdriver features, lets you use Stackdriver
  // with AWS accounts, and has a larger allotments for logs and metrics. For
  // more details, see [the service tiers
  // documentation](https://cloud.google.com/monitoring/workspaces/tiers).
  SERVICE_TIER_PREMIUM = 2;
}