dashboard/v1/common.proto

// Copyright 2023 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.dashboard.v1;

import "google/protobuf/duration.proto";
import "google/type/interval.proto";

option csharp_namespace = "Google.Cloud.Monitoring.Dashboard.V1";
option go_package = "cloud.google.com/go/monitoring/dashboard/apiv1/dashboardpb;dashboardpb";
option java_multiple_files = true;
option java_outer_classname = "CommonProto";
option java_package = "com.google.monitoring.dashboard.v1";
option php_namespace = "Google\\Cloud\\Monitoring\\Dashboard\\V1";
option ruby_package = "Google::Cloud::Monitoring::Dashboard::V1";

// 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 2 years, or 104 weeks.
  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;
}

// Describes a ranking-based time series filter. Each input time series is
// ranked with an aligner. The filter will allow up to `num_time_series` time
// series to pass through it, selecting them based on the relative ranking.
//
// For example, if `ranking_method` is `METHOD_MEAN`,`direction` is `BOTTOM`,
// and `num_time_series` is 3, then the 3 times series with the lowest mean
// values will pass through the filter.
message PickTimeSeriesFilter {
  // The value reducers that can be applied to a `PickTimeSeriesFilter`.
  enum Method {
    // Not allowed. You must specify a different `Method` if you specify a
    // `PickTimeSeriesFilter`.
    METHOD_UNSPECIFIED = 0;

    // Select the mean of all values.
    METHOD_MEAN = 1;

    // Select the maximum value.
    METHOD_MAX = 2;

    // Select the minimum value.
    METHOD_MIN = 3;

    // Compute the sum of all values.
    METHOD_SUM = 4;

    // Select the most recent value.
    METHOD_LATEST = 5;
  }

  // Describes the ranking directions.
  enum Direction {
    // Not allowed. You must specify a different `Direction` if you specify a
    // `PickTimeSeriesFilter`.
    DIRECTION_UNSPECIFIED = 0;

    // Pass the highest `num_time_series` ranking inputs.
    TOP = 1;

    // Pass the lowest `num_time_series` ranking inputs.
    BOTTOM = 2;
  }

  // `ranking_method` is applied to each time series independently to produce
  // the value which will be used to compare the time series to other time
  // series.
  Method ranking_method = 1;

  // How many time series to allow to pass through the filter.
  int32 num_time_series = 2;

  // How to use the ranking to select time series that pass through the filter.
  Direction direction = 3;

  // Select the top N streams/time series within this time interval
  google.type.Interval interval = 4;
}

// A filter that ranks streams based on their statistical relation to other
// streams in a request.
// Note: This field is deprecated and completely ignored by the API.
message StatisticalTimeSeriesFilter {
  // The filter methods that can be applied to a stream.
  enum Method {
    // Not allowed in well-formed requests.
    METHOD_UNSPECIFIED = 0;

    // Compute the outlier score of each stream.
    METHOD_CLUSTER_OUTLIER = 1;
  }

  // `rankingMethod` is applied to a set of time series, and then the produced
  // value for each individual time series is used to compare a given time
  // series to others.
  // These are methods that cannot be applied stream-by-stream, but rather
  // require the full context of a request to evaluate time series.
  Method ranking_method = 1;

  // How many time series to output.
  int32 num_time_series = 2;
}