third_party/googleapis/google/monitoring/v3/common.proto - bazel - Git at Google

 // Copyright 2016 Google Inc.
 //
 // 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 = "google.golang.org/genproto/googleapis/monitoring/v3;monitoring";
 option java_multiple_files = true;
 option java_outer_classname = "CommonProto";
 option java_package = "com.google.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 time interval extending just after a start time through an end time.
 // If the start time is the same as the end time, then the interval
 // represents a single point in 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 different views of
 // the data.  Aggregation consists of an alignment step on individual time
 // series (`per_series_aligner`) followed by an optional reduction of the data
 // across different time series (`cross_series_reducer`).  For more details, see
 // [Aggregation](/monitoring/api/learn_more#aggregation).
 message Aggregation {
   // The Aligner describes how to bring the data points in a single
   // time series into temporal alignment.
   enum Aligner {
     // No alignment. Raw data is returned. Not valid if cross-time
     // 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 metric type. This alignment is valid
     // for cumulative metrics and delta metrics. Aligning an existing
     // delta metric to a delta metric requires that the alignment
     // period be increased. The value type of the result is the same
     // as the value type of the input.
     ALIGN_DELTA = 1;

     // Align and convert to a rate. This alignment is valid for
     // cumulative metrics and delta metrics with numeric values. The output is a
     // gauge metric with value type
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     ALIGN_RATE = 2;

     // Align by interpolating between adjacent points around the
     // period boundary. This alignment is valid for gauge
     // metrics with numeric values. The value type of the result is the same
     // as the value type of the input.
     ALIGN_INTERPOLATE = 3;

     // Align by shifting the oldest data point before the period
     // boundary to the boundary. This alignment is valid for gauge
     // metrics. The value type of the result is the same as the
     // value type of the input.
     ALIGN_NEXT_OLDER = 4;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the minimum of all data points in the
     // period. This alignment is valid for gauge and delta metrics with numeric
     // values. The value type of the result is the same as the value
     // type of the input.
     ALIGN_MIN = 10;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the maximum of all data points in the
     // period. This alignment is valid for gauge and delta metrics with numeric
     // values. The value type of the result is the same as the value
     // type of the input.
     ALIGN_MAX = 11;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the average or arithmetic mean of all
     // data points in the period. This alignment is valid for gauge and delta
     // metrics with numeric values. The value type of the output is
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     ALIGN_MEAN = 12;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the count of all data points in the
     // period. This alignment is valid for gauge and delta metrics with numeric
     // or Boolean values. The value type of the output is
     // [INT64][google.api.MetricDescriptor.ValueType.INT64].
     ALIGN_COUNT = 13;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the sum of all data points in the
     // period. This alignment is valid for gauge and delta metrics with numeric
     // and distribution values. The value type of the output is the
     // same as the value type of the input.
     ALIGN_SUM = 14;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the standard deviation of all data
     // points in the period. This alignment is valid for gauge and delta metrics
     // with numeric values. The value type of the output is
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     ALIGN_STDDEV = 15;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the count of True-valued data points in the
     // period. This alignment is valid for gauge metrics with
     // Boolean values. The value type of the output is
     // [INT64][google.api.MetricDescriptor.ValueType.INT64].
     ALIGN_COUNT_TRUE = 16;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the fraction of True-valued data points in the
     // period. This alignment is valid for gauge metrics with Boolean values.
     // The output value is in the range [0, 1] and has value type
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     ALIGN_FRACTION_TRUE = 17;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the 99th percentile of all data
     // points in the period. This alignment is valid for gauge and delta metrics
     // with distribution values. The output is a gauge metric with value type
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     ALIGN_PERCENTILE_99 = 18;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the 95th percentile of all data
     // points in the period. This alignment is valid for gauge and delta metrics
     // with distribution values. The output is a gauge metric with value type
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     ALIGN_PERCENTILE_95 = 19;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the 50th percentile of all data
     // points in the period. This alignment is valid for gauge and delta metrics
     // with distribution values. The output is a gauge metric with value type
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     ALIGN_PERCENTILE_50 = 20;

     // Align time series via aggregation. The resulting data point in
     // the alignment period is the 5th percentile of all data
     // points in the period. This alignment is valid for gauge and delta metrics
     // with distribution values. The output is a gauge metric with value type
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     ALIGN_PERCENTILE_05 = 21;
   }

   // A Reducer describes how to aggregate data points from multiple
   // time series into a single time series.
   enum Reducer {
     // No cross-time series reduction. The output of the aligner is
     // returned.
     REDUCE_NONE = 0;

     // Reduce by computing the mean 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][google.api.MetricDescriptor.ValueType.DOUBLE].
     REDUCE_MEAN = 1;

     // Reduce by computing the minimum 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 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][google.api.MetricDescriptor.ValueType.DOUBLE].
     REDUCE_STDDEV = 5;

     // Reduce by computing the count 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][google.api.MetricDescriptor.ValueType.INT64].
     REDUCE_COUNT = 6;

     // Reduce by computing the count 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][google.api.MetricDescriptor.ValueType.INT64].
     REDUCE_COUNT_TRUE = 7;

     // Reduce by computing the fraction 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 output value is in the
     // range [0, 1] and has value type
     // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
     REDUCE_FRACTION_TRUE = 8;

     // Reduce by computing 99th 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][google.api.MetricDescriptor.ValueType.DOUBLE]
     REDUCE_PERCENTILE_99 = 9;

     // Reduce by computing 95th 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][google.api.MetricDescriptor.ValueType.DOUBLE]
     REDUCE_PERCENTILE_95 = 10;

     // Reduce by computing 50th 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][google.api.MetricDescriptor.ValueType.DOUBLE]
     REDUCE_PERCENTILE_50 = 11;

     // Reduce by computing 5th 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][google.api.MetricDescriptor.ValueType.DOUBLE]
     REDUCE_PERCENTILE_05 = 12;
   }

   // The alignment period for per-[time series][google.monitoring.v3.TimeSeries]
   // alignment. If present, `alignmentPeriod` must be at least 60
   // seconds.  After per-time series alignment, each time series will
   // contain data points only on the period boundaries. If
   // `perSeriesAligner` is not specified or equals `ALIGN_NONE`, then
   // this field is ignored. If `perSeriesAligner` is specified and
   // does not equal `ALIGN_NONE`, then this field must be defined;
   // otherwise an error is returned.
   google.protobuf.Duration alignment_period = 1;

   // The approach to be used to align individual time series. Not all
   // alignment functions may be applied to all time series, depending
   // on the metric type and value type of the original time
   // series. Alignment may change the metric type or the value type of
   // the time series.
   //
   // Time series data must be aligned in order to perform cross-time
   // series reduction. If `crossSeriesReducer` is specified, then
   // `perSeriesAligner` must be specified and not equal `ALIGN_NONE`
   // and `alignmentPeriod` must be specified; otherwise, an error is
   // returned.
   Aligner per_series_aligner = 2;

   // The approach to be used to combine time series. Not all reducer
   // functions may be applied to all time series, depending on the
   // metric type and the value type of the original time
   // series. Reduction may change the metric type of value type of the
   // time series.
   //
   // Time series data must be aligned in order to perform cross-time
   // series reduction. If `crossSeriesReducer` is specified, then
   // `perSeriesAligner` must be specified and not equal `ALIGN_NONE`
   // and `alignmentPeriod` must be specified; otherwise, an error is
   // returned.
   Reducer cross_series_reducer = 4;

   // The set of fields to preserve when `crossSeriesReducer` is
   // specified. The `groupByFields` determine how the time series are
   // partitioned into subsets prior to applying the aggregation
   // function. 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
   // `crossSeriesReducer` 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 `groupByFields` are aggregated away.  If
   // `groupByFields` 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 `crossSeriesReducer` is not
   // defined, this field is ignored.
   repeated string group_by_fields = 5;
 }
	// Copyright 2016 Google Inc.
	//
	// 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 = "google.golang.org/genproto/googleapis/monitoring/v3;monitoring";
	option java_multiple_files = true;
	option java_outer_classname = "CommonProto";
	option java_package = "com.google.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 ±9.2x10<sup>18</sup>.
	int64 int64_value = 2;

	// A 64-bit double-precision floating-point number. Its magnitude
	// is approximately ±10<sup>±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 time interval extending just after a start time through an end time.
	// If the start time is the same as the end time, then the interval
	// represents a single point in 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 different views of
	// the data. Aggregation consists of an alignment step on individual time
	// series (`per_series_aligner`) followed by an optional reduction of the data
	// across different time series (`cross_series_reducer`). For more details, see
	// [Aggregation](/monitoring/api/learn_more#aggregation).
	message Aggregation {
	// The Aligner describes how to bring the data points in a single
	// time series into temporal alignment.
	enum Aligner {
	// No alignment. Raw data is returned. Not valid if cross-time
	// 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 metric type. This alignment is valid
	// for cumulative metrics and delta metrics. Aligning an existing
	// delta metric to a delta metric requires that the alignment
	// period be increased. The value type of the result is the same
	// as the value type of the input.
	ALIGN_DELTA = 1;

	// Align and convert to a rate. This alignment is valid for
	// cumulative metrics and delta metrics with numeric values. The output is a
	// gauge metric with value type
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	ALIGN_RATE = 2;

	// Align by interpolating between adjacent points around the
	// period boundary. This alignment is valid for gauge
	// metrics with numeric values. The value type of the result is the same
	// as the value type of the input.
	ALIGN_INTERPOLATE = 3;

	// Align by shifting the oldest data point before the period
	// boundary to the boundary. This alignment is valid for gauge
	// metrics. The value type of the result is the same as the
	// value type of the input.
	ALIGN_NEXT_OLDER = 4;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the minimum of all data points in the
	// period. This alignment is valid for gauge and delta metrics with numeric
	// values. The value type of the result is the same as the value
	// type of the input.
	ALIGN_MIN = 10;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the maximum of all data points in the
	// period. This alignment is valid for gauge and delta metrics with numeric
	// values. The value type of the result is the same as the value
	// type of the input.
	ALIGN_MAX = 11;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the average or arithmetic mean of all
	// data points in the period. This alignment is valid for gauge and delta
	// metrics with numeric values. The value type of the output is
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	ALIGN_MEAN = 12;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the count of all data points in the
	// period. This alignment is valid for gauge and delta metrics with numeric
	// or Boolean values. The value type of the output is
	// [INT64][google.api.MetricDescriptor.ValueType.INT64].
	ALIGN_COUNT = 13;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the sum of all data points in the
	// period. This alignment is valid for gauge and delta metrics with numeric
	// and distribution values. The value type of the output is the
	// same as the value type of the input.
	ALIGN_SUM = 14;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the standard deviation of all data
	// points in the period. This alignment is valid for gauge and delta metrics
	// with numeric values. The value type of the output is
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	ALIGN_STDDEV = 15;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the count of True-valued data points in the
	// period. This alignment is valid for gauge metrics with
	// Boolean values. The value type of the output is
	// [INT64][google.api.MetricDescriptor.ValueType.INT64].
	ALIGN_COUNT_TRUE = 16;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the fraction of True-valued data points in the
	// period. This alignment is valid for gauge metrics with Boolean values.
	// The output value is in the range [0, 1] and has value type
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	ALIGN_FRACTION_TRUE = 17;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the 99th percentile of all data
	// points in the period. This alignment is valid for gauge and delta metrics
	// with distribution values. The output is a gauge metric with value type
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	ALIGN_PERCENTILE_99 = 18;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the 95th percentile of all data
	// points in the period. This alignment is valid for gauge and delta metrics
	// with distribution values. The output is a gauge metric with value type
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	ALIGN_PERCENTILE_95 = 19;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the 50th percentile of all data
	// points in the period. This alignment is valid for gauge and delta metrics
	// with distribution values. The output is a gauge metric with value type
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	ALIGN_PERCENTILE_50 = 20;

	// Align time series via aggregation. The resulting data point in
	// the alignment period is the 5th percentile of all data
	// points in the period. This alignment is valid for gauge and delta metrics
	// with distribution values. The output is a gauge metric with value type
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	ALIGN_PERCENTILE_05 = 21;
	}

	// A Reducer describes how to aggregate data points from multiple
	// time series into a single time series.
	enum Reducer {
	// No cross-time series reduction. The output of the aligner is
	// returned.
	REDUCE_NONE = 0;

	// Reduce by computing the mean 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][google.api.MetricDescriptor.ValueType.DOUBLE].
	REDUCE_MEAN = 1;

	// Reduce by computing the minimum 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 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][google.api.MetricDescriptor.ValueType.DOUBLE].
	REDUCE_STDDEV = 5;

	// Reduce by computing the count 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][google.api.MetricDescriptor.ValueType.INT64].
	REDUCE_COUNT = 6;

	// Reduce by computing the count 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][google.api.MetricDescriptor.ValueType.INT64].
	REDUCE_COUNT_TRUE = 7;

	// Reduce by computing the fraction 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 output value is in the
	// range [0, 1] and has value type
	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
	REDUCE_FRACTION_TRUE = 8;

	// Reduce by computing 99th 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][google.api.MetricDescriptor.ValueType.DOUBLE]
	REDUCE_PERCENTILE_99 = 9;

	// Reduce by computing 95th 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][google.api.MetricDescriptor.ValueType.DOUBLE]
	REDUCE_PERCENTILE_95 = 10;

	// Reduce by computing 50th 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][google.api.MetricDescriptor.ValueType.DOUBLE]
	REDUCE_PERCENTILE_50 = 11;

	// Reduce by computing 5th 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][google.api.MetricDescriptor.ValueType.DOUBLE]
	REDUCE_PERCENTILE_05 = 12;
	}

	// The alignment period for per-[time series][google.monitoring.v3.TimeSeries]
	// alignment. If present, `alignmentPeriod` must be at least 60
	// seconds. After per-time series alignment, each time series will
	// contain data points only on the period boundaries. If
	// `perSeriesAligner` is not specified or equals `ALIGN_NONE`, then
	// this field is ignored. If `perSeriesAligner` is specified and
	// does not equal `ALIGN_NONE`, then this field must be defined;
	// otherwise an error is returned.
	google.protobuf.Duration alignment_period = 1;

	// The approach to be used to align individual time series. Not all
	// alignment functions may be applied to all time series, depending
	// on the metric type and value type of the original time
	// series. Alignment may change the metric type or the value type of
	// the time series.
	//
	// Time series data must be aligned in order to perform cross-time
	// series reduction. If `crossSeriesReducer` is specified, then
	// `perSeriesAligner` must be specified and not equal `ALIGN_NONE`
	// and `alignmentPeriod` must be specified; otherwise, an error is
	// returned.
	Aligner per_series_aligner = 2;

	// The approach to be used to combine time series. Not all reducer
	// functions may be applied to all time series, depending on the
	// metric type and the value type of the original time
	// series. Reduction may change the metric type of value type of the
	// time series.
	//
	// Time series data must be aligned in order to perform cross-time
	// series reduction. If `crossSeriesReducer` is specified, then
	// `perSeriesAligner` must be specified and not equal `ALIGN_NONE`
	// and `alignmentPeriod` must be specified; otherwise, an error is
	// returned.
	Reducer cross_series_reducer = 4;

	// The set of fields to preserve when `crossSeriesReducer` is
	// specified. The `groupByFields` determine how the time series are
	// partitioned into subsets prior to applying the aggregation
	// function. 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
	// `crossSeriesReducer` 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 `groupByFields` are aggregated away. If
	// `groupByFields` 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 `crossSeriesReducer` is not
	// defined, this field is ignored.
	repeated string group_by_fields = 5;
	}