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Luke Sneeringer
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Updated auto-gen client for monitoring.
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23 files changed

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‎packages/monitoring/package.json‎

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],
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"dependencies": {
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"extend": "^3.0",
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"google-gax": "^0.13.2",
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"google-proto-files": "^0.12.0"
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"google-gax": "^0.14.2",
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"lodash.merge": "^4.6.0",
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"lodash.union": "^4.6.0"
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},
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"devDependencies": {
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"mocha": "^3.2.0"

‎packages/monitoring/protos/google/monitoring/v3/common.proto‎

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// Copyright 2016 Google Inc.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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syntax = "proto3";
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package google.monitoring.v3;
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import "google/api/distribution.proto";
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import "google/protobuf/duration.proto";
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import "google/protobuf/timestamp.proto";
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option csharp_namespace = "Google.Cloud.Monitoring.V3";
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option go_package = "google.golang.org/genproto/googleapis/monitoring/v3;monitoring";
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option java_multiple_files = true;
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option java_outer_classname = "CommonProto";
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option java_package = "com.google.monitoring.v3";
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// A single strongly-typed value.
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message TypedValue {
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// The typed value field.
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oneof value {
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// A Boolean value: `true` or `false`.
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bool bool_value = 1;
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// A 64-bit integer. Its range is approximately &plusmn;9.2x10<sup>18</sup>.
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int64 int64_value = 2;
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// A 64-bit double-precision floating-point number. Its magnitude
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// is approximately &plusmn;10<sup>&plusmn;300</sup> and it has 16
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// significant digits of precision.
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double double_value = 3;
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// A variable-length string value.
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string string_value = 4;
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// A distribution value.
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google.api.Distribution distribution_value = 5;
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}
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}
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// A time interval extending just after a start time through an end time.
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// If the start time is the same as the end time, then the interval
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// represents a single point in time.
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message TimeInterval {
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// Required. The end of the time interval.
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google.protobuf.Timestamp end_time = 2;
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// Optional. The beginning of the time interval. The default value
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// for the start time is the end time. The start time must not be
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// later than the end time.
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google.protobuf.Timestamp start_time = 1;
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}
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// Describes how to combine multiple time series to provide different views of
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// the data. Aggregation consists of an alignment step on individual time
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// series (`per_series_aligner`) followed by an optional reduction of the data
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// across different time series (`cross_series_reducer`). For more details, see
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// [Aggregation](/monitoring/api/learn_more#aggregation).
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message Aggregation {
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// The Aligner describes how to bring the data points in a single
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// time series into temporal alignment.
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enum Aligner {
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// No alignment. Raw data is returned. Not valid if cross-time
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// series reduction is requested. The value type of the result is
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// the same as the value type of the input.
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ALIGN_NONE = 0;
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// Align and convert to delta metric type. This alignment is valid
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// for cumulative metrics and delta metrics. Aligning an existing
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// delta metric to a delta metric requires that the alignment
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// period be increased. The value type of the result is the same
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// as the value type of the input.
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ALIGN_DELTA = 1;
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// Align and convert to a rate. This alignment is valid for
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// cumulative metrics and delta metrics with numeric values. The output is a
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// gauge metric with value type
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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ALIGN_RATE = 2;
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// Align by interpolating between adjacent points around the
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// period boundary. This alignment is valid for gauge
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// metrics with numeric values. The value type of the result is the same
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// as the value type of the input.
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ALIGN_INTERPOLATE = 3;
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// Align by shifting the oldest data point before the period
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// boundary to the boundary. This alignment is valid for gauge
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// metrics. The value type of the result is the same as the
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// value type of the input.
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ALIGN_NEXT_OLDER = 4;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the minimum of all data points in the
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// period. This alignment is valid for gauge and delta metrics with numeric
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// values. The value type of the result is the same as the value
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// type of the input.
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ALIGN_MIN = 10;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the maximum of all data points in the
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// period. This alignment is valid for gauge and delta metrics with numeric
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// values. The value type of the result is the same as the value
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// type of the input.
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ALIGN_MAX = 11;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the average or arithmetic mean of all
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// data points in the period. This alignment is valid for gauge and delta
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// metrics with numeric values. The value type of the output is
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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ALIGN_MEAN = 12;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the count of all data points in the
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// period. This alignment is valid for gauge and delta metrics with numeric
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// or Boolean values. The value type of the output is
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// [INT64][google.api.MetricDescriptor.ValueType.INT64].
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ALIGN_COUNT = 13;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the sum of all data points in the
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// period. This alignment is valid for gauge and delta metrics with numeric
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// and distribution values. The value type of the output is the
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// same as the value type of the input.
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ALIGN_SUM = 14;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the standard deviation of all data
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// points in the period. This alignment is valid for gauge and delta metrics
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// with numeric values. The value type of the output is
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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ALIGN_STDDEV = 15;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the count of True-valued data points in the
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// period. This alignment is valid for gauge metrics with
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// Boolean values. The value type of the output is
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// [INT64][google.api.MetricDescriptor.ValueType.INT64].
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ALIGN_COUNT_TRUE = 16;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the fraction of True-valued data points in the
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// period. This alignment is valid for gauge metrics with Boolean values.
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// The output value is in the range [0, 1] and has value type
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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ALIGN_FRACTION_TRUE = 17;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the 99th percentile of all data
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// points in the period. This alignment is valid for gauge and delta metrics
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// with distribution values. The output is a gauge metric with value type
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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ALIGN_PERCENTILE_99 = 18;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the 95th percentile of all data
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// points in the period. This alignment is valid for gauge and delta metrics
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// with distribution values. The output is a gauge metric with value type
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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ALIGN_PERCENTILE_95 = 19;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the 50th percentile of all data
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// points in the period. This alignment is valid for gauge and delta metrics
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// with distribution values. The output is a gauge metric with value type
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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ALIGN_PERCENTILE_50 = 20;
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// Align time series via aggregation. The resulting data point in
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// the alignment period is the 5th percentile of all data
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// points in the period. This alignment is valid for gauge and delta metrics
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// with distribution values. The output is a gauge metric with value type
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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ALIGN_PERCENTILE_05 = 21;
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}
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// A Reducer describes how to aggregate data points from multiple
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// time series into a single time series.
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enum Reducer {
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// No cross-time series reduction. The output of the aligner is
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// returned.
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REDUCE_NONE = 0;
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// Reduce by computing the mean across time series for each
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// alignment period. This reducer is valid for delta and
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// gauge metrics with numeric or distribution values. The value type of the
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// output is [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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REDUCE_MEAN = 1;
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// Reduce by computing the minimum across time series for each
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// alignment period. This reducer is valid for delta and
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// gauge metrics with numeric values. The value type of the output
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// is the same as the value type of the input.
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REDUCE_MIN = 2;
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// Reduce by computing the maximum across time series for each
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// alignment period. This reducer is valid for delta and
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// gauge metrics with numeric values. The value type of the output
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// is the same as the value type of the input.
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REDUCE_MAX = 3;
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// Reduce by computing the sum across time series for each
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// alignment period. This reducer is valid for delta and
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// gauge metrics with numeric and distribution values. The value type of
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// the output is the same as the value type of the input.
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REDUCE_SUM = 4;
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// Reduce by computing the standard deviation across time series
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// for each alignment period. This reducer is valid for delta
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// and gauge metrics with numeric or distribution values. The value type of
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// the output is [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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REDUCE_STDDEV = 5;
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// Reduce by computing the count of data points across time series
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// for each alignment period. This reducer is valid for delta
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// and gauge metrics of numeric, Boolean, distribution, and string value
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// type. The value type of the output is
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// [INT64][google.api.MetricDescriptor.ValueType.INT64].
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REDUCE_COUNT = 6;
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// Reduce by computing the count of True-valued data points across time
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// series for each alignment period. This reducer is valid for delta
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// and gauge metrics of Boolean value type. The value type of
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// the output is [INT64][google.api.MetricDescriptor.ValueType.INT64].
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REDUCE_COUNT_TRUE = 7;
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// Reduce by computing the fraction of True-valued data points across time
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// series for each alignment period. This reducer is valid for delta
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// and gauge metrics of Boolean value type. The output value is in the
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// range [0, 1] and has value type
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
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REDUCE_FRACTION_TRUE = 8;
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// Reduce by computing 99th percentile of data points across time series
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// for each alignment period. This reducer is valid for gauge and delta
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// metrics of numeric and distribution type. The value of the output is
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE]
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REDUCE_PERCENTILE_99 = 9;
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// Reduce by computing 95th percentile of data points across time series
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// for each alignment period. This reducer is valid for gauge and delta
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// metrics of numeric and distribution type. The value of the output is
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE]
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REDUCE_PERCENTILE_95 = 10;
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// Reduce by computing 50th percentile of data points across time series
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// for each alignment period. This reducer is valid for gauge and delta
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// metrics of numeric and distribution type. The value of the output is
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE]
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REDUCE_PERCENTILE_50 = 11;
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// Reduce by computing 5th percentile of data points across time series
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// for each alignment period. This reducer is valid for gauge and delta
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// metrics of numeric and distribution type. The value of the output is
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// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE]
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REDUCE_PERCENTILE_05 = 12;
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}
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// The alignment period for per-[time series][google.monitoring.v3.TimeSeries]
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// alignment. If present, `alignmentPeriod` must be at least 60
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// seconds. After per-time series alignment, each time series will
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// contain data points only on the period boundaries. If
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// `perSeriesAligner` is not specified or equals `ALIGN_NONE`, then
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// this field is ignored. If `perSeriesAligner` is specified and
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// does not equal `ALIGN_NONE`, then this field must be defined;
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// otherwise an error is returned.
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google.protobuf.Duration alignment_period = 1;
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// The approach to be used to align individual time series. Not all
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// alignment functions may be applied to all time series, depending
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// on the metric type and value type of the original time
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// series. Alignment may change the metric type or the value type of
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// the time series.
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//
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// Time series data must be aligned in order to perform cross-time
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// series reduction. If `crossSeriesReducer` is specified, then
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// `perSeriesAligner` must be specified and not equal `ALIGN_NONE`
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// and `alignmentPeriod` must be specified; otherwise, an error is
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// returned.
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Aligner per_series_aligner = 2;
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// The approach to be used to combine time series. Not all reducer
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// functions may be applied to all time series, depending on the
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// metric type and the value type of the original time
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// series. Reduction may change the metric type of value type of the
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// time series.
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//
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// Time series data must be aligned in order to perform cross-time
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// series reduction. If `crossSeriesReducer` is specified, then
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// `perSeriesAligner` must be specified and not equal `ALIGN_NONE`
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// and `alignmentPeriod` must be specified; otherwise, an error is
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// returned.
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Reducer cross_series_reducer = 4;
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// The set of fields to preserve when `crossSeriesReducer` is
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// specified. The `groupByFields` determine how the time series are
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// partitioned into subsets prior to applying the aggregation
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// function. Each subset contains time series that have the same
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// value for each of the grouping fields. Each individual time
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// series is a member of exactly one subset. The
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// `crossSeriesReducer` is applied to each subset of time series.
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// It is not possible to reduce across different resource types, so
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// this field implicitly contains `resource.type`. Fields not
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// specified in `groupByFields` are aggregated away. If
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// `groupByFields` is not specified and all the time series have
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// the same resource type, then the time series are aggregated into
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// a single output time series. If `crossSeriesReducer` is not
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// defined, this field is ignored.
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repeated string group_by_fields = 5;
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}

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