Source file src/cloud.google.com/go/monitoring/dashboard/apiv1/dashboardpb/common.pb.go

Documentation: cloud.google.com/go/monitoring/dashboard/apiv1/dashboardpb

     1  // Copyright 2023 Google LLC
     2  //
     3  // Licensed under the Apache License, Version 2.0 (the "License");
     4  // you may not use this file except in compliance with the License.
     5  // You may obtain a copy of the License at
     6  //
     7  //     http://www.apache.org/licenses/LICENSE-2.0
     8  //
     9  // Unless required by applicable law or agreed to in writing, software
    10  // distributed under the License is distributed on an "AS IS" BASIS,
    11  // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    12  // See the License for the specific language governing permissions and
    13  // limitations under the License.
    14  
    15  // Code generated by protoc-gen-go. DO NOT EDIT.
    16  // versions:
    17  // 	protoc-gen-go v1.33.0
    18  // 	protoc        v4.25.3
    19  // source: google/monitoring/dashboard/v1/common.proto
    20  
    21  package dashboardpb
    22  
    23  import (
    24  	reflect "reflect"
    25  	sync "sync"
    26  
    27  	interval "google.golang.org/genproto/googleapis/type/interval"
    28  	protoreflect "google.golang.org/protobuf/reflect/protoreflect"
    29  	protoimpl "google.golang.org/protobuf/runtime/protoimpl"
    30  	durationpb "google.golang.org/protobuf/types/known/durationpb"
    31  )
    32  
    33  const (
    34  	// Verify that this generated code is sufficiently up-to-date.
    35  	_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
    36  	// Verify that runtime/protoimpl is sufficiently up-to-date.
    37  	_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
    38  )
    39  
    40  // The `Aligner` specifies the operation that will be applied to the data
    41  // points in each alignment period in a time series. Except for
    42  // `ALIGN_NONE`, which specifies that no operation be applied, each alignment
    43  // operation replaces the set of data values in each alignment period with
    44  // a single value: the result of applying the operation to the data values.
    45  // An aligned time series has a single data value at the end of each
    46  // `alignment_period`.
    47  //
    48  // An alignment operation can change the data type of the values, too. For
    49  // example, if you apply a counting operation to boolean values, the data
    50  // `value_type` in the original time series is `BOOLEAN`, but the `value_type`
    51  // in the aligned result is `INT64`.
    52  type Aggregation_Aligner int32
    53  
    54  const (
    55  	// No alignment. Raw data is returned. Not valid if cross-series reduction
    56  	// is requested. The `value_type` of the result is the same as the
    57  	// `value_type` of the input.
    58  	Aggregation_ALIGN_NONE Aggregation_Aligner = 0
    59  	// Align and convert to
    60  	// [DELTA][google.api.MetricDescriptor.MetricKind.DELTA].
    61  	// The output is `delta = y1 - y0`.
    62  	//
    63  	// This alignment is valid for
    64  	// [CUMULATIVE][google.api.MetricDescriptor.MetricKind.CUMULATIVE] and
    65  	// `DELTA` metrics. If the selected alignment period results in periods
    66  	// with no data, then the aligned value for such a period is created by
    67  	// interpolation. The `value_type`  of the aligned result is the same as
    68  	// the `value_type` of the input.
    69  	Aggregation_ALIGN_DELTA Aggregation_Aligner = 1
    70  	// Align and convert to a rate. The result is computed as
    71  	// `rate = (y1 - y0)/(t1 - t0)`, or "delta over time".
    72  	// Think of this aligner as providing the slope of the line that passes
    73  	// through the value at the start and at the end of the `alignment_period`.
    74  	//
    75  	// This aligner is valid for `CUMULATIVE`
    76  	// and `DELTA` metrics with numeric values. If the selected alignment
    77  	// period results in periods with no data, then the aligned value for
    78  	// such a period is created by interpolation. The output is a `GAUGE`
    79  	// metric with `value_type` `DOUBLE`.
    80  	//
    81  	// If, by "rate", you mean "percentage change", see the
    82  	// `ALIGN_PERCENT_CHANGE` aligner instead.
    83  	Aggregation_ALIGN_RATE Aggregation_Aligner = 2
    84  	// Align by interpolating between adjacent points around the alignment
    85  	// period boundary. This aligner is valid for `GAUGE` metrics with
    86  	// numeric values. The `value_type` of the aligned result is the same as the
    87  	// `value_type` of the input.
    88  	Aggregation_ALIGN_INTERPOLATE Aggregation_Aligner = 3
    89  	// Align by moving the most recent data point before the end of the
    90  	// alignment period to the boundary at the end of the alignment
    91  	// period. This aligner is valid for `GAUGE` metrics. The `value_type` of
    92  	// the aligned result is the same as the `value_type` of the input.
    93  	Aggregation_ALIGN_NEXT_OLDER Aggregation_Aligner = 4
    94  	// Align the time series by returning the minimum value in each alignment
    95  	// period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    96  	// numeric values. The `value_type` of the aligned result is the same as
    97  	// the `value_type` of the input.
    98  	Aggregation_ALIGN_MIN Aggregation_Aligner = 10
    99  	// Align the time series by returning the maximum value in each alignment
   100  	// period. This aligner is valid for `GAUGE` and `DELTA` metrics with
   101  	// numeric values. The `value_type` of the aligned result is the same as
   102  	// the `value_type` of the input.
   103  	Aggregation_ALIGN_MAX Aggregation_Aligner = 11
   104  	// Align the time series by returning the mean value in each alignment
   105  	// period. This aligner is valid for `GAUGE` and `DELTA` metrics with
   106  	// numeric values. The `value_type` of the aligned result is `DOUBLE`.
   107  	Aggregation_ALIGN_MEAN Aggregation_Aligner = 12
   108  	// Align the time series by returning the number of values in each alignment
   109  	// period. This aligner is valid for `GAUGE` and `DELTA` metrics with
   110  	// numeric or Boolean values. The `value_type` of the aligned result is
   111  	// `INT64`.
   112  	Aggregation_ALIGN_COUNT Aggregation_Aligner = 13
   113  	// Align the time series by returning the sum of the values in each
   114  	// alignment period. This aligner is valid for `GAUGE` and `DELTA`
   115  	// metrics with numeric and distribution values. The `value_type` of the
   116  	// aligned result is the same as the `value_type` of the input.
   117  	Aggregation_ALIGN_SUM Aggregation_Aligner = 14
   118  	// Align the time series by returning the standard deviation of the values
   119  	// in each alignment period. This aligner is valid for `GAUGE` and
   120  	// `DELTA` metrics with numeric values. The `value_type` of the output is
   121  	// `DOUBLE`.
   122  	Aggregation_ALIGN_STDDEV Aggregation_Aligner = 15
   123  	// Align the time series by returning the number of `True` values in
   124  	// each alignment period. This aligner is valid for `GAUGE` metrics with
   125  	// Boolean values. The `value_type` of the output is `INT64`.
   126  	Aggregation_ALIGN_COUNT_TRUE Aggregation_Aligner = 16
   127  	// Align the time series by returning the number of `False` values in
   128  	// each alignment period. This aligner is valid for `GAUGE` metrics with
   129  	// Boolean values. The `value_type` of the output is `INT64`.
   130  	Aggregation_ALIGN_COUNT_FALSE Aggregation_Aligner = 24
   131  	// Align the time series by returning the ratio of the number of `True`
   132  	// values to the total number of values in each alignment period. This
   133  	// aligner is valid for `GAUGE` metrics with Boolean values. The output
   134  	// value is in the range [0.0, 1.0] and has `value_type` `DOUBLE`.
   135  	Aggregation_ALIGN_FRACTION_TRUE Aggregation_Aligner = 17
   136  	// Align the time series by using [percentile
   137  	// aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
   138  	// data point in each alignment period is the 99th percentile of all data
   139  	// points in the period. This aligner is valid for `GAUGE` and `DELTA`
   140  	// metrics with distribution values. The output is a `GAUGE` metric with
   141  	// `value_type` `DOUBLE`.
   142  	Aggregation_ALIGN_PERCENTILE_99 Aggregation_Aligner = 18
   143  	// Align the time series by using [percentile
   144  	// aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
   145  	// data point in each alignment period is the 95th percentile of all data
   146  	// points in the period. This aligner is valid for `GAUGE` and `DELTA`
   147  	// metrics with distribution values. The output is a `GAUGE` metric with
   148  	// `value_type` `DOUBLE`.
   149  	Aggregation_ALIGN_PERCENTILE_95 Aggregation_Aligner = 19
   150  	// Align the time series by using [percentile
   151  	// aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
   152  	// data point in each alignment period is the 50th percentile of all data
   153  	// points in the period. This aligner is valid for `GAUGE` and `DELTA`
   154  	// metrics with distribution values. The output is a `GAUGE` metric with
   155  	// `value_type` `DOUBLE`.
   156  	Aggregation_ALIGN_PERCENTILE_50 Aggregation_Aligner = 20
   157  	// Align the time series by using [percentile
   158  	// aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
   159  	// data point in each alignment period is the 5th percentile of all data
   160  	// points in the period. This aligner is valid for `GAUGE` and `DELTA`
   161  	// metrics with distribution values. The output is a `GAUGE` metric with
   162  	// `value_type` `DOUBLE`.
   163  	Aggregation_ALIGN_PERCENTILE_05 Aggregation_Aligner = 21
   164  	// Align and convert to a percentage change. This aligner is valid for
   165  	// `GAUGE` and `DELTA` metrics with numeric values. This alignment returns
   166  	// `((current - previous)/previous) * 100`, where the value of `previous` is
   167  	// determined based on the `alignment_period`.
   168  	//
   169  	// If the values of `current` and `previous` are both 0, then the returned
   170  	// value is 0. If only `previous` is 0, the returned value is infinity.
   171  	//
   172  	// A 10-minute moving mean is computed at each point of the alignment period
   173  	// prior to the above calculation to smooth the metric and prevent false
   174  	// positives from very short-lived spikes. The moving mean is only
   175  	// applicable for data whose values are `>= 0`. Any values `< 0` are
   176  	// treated as a missing datapoint, and are ignored. While `DELTA`
   177  	// metrics are accepted by this alignment, special care should be taken that
   178  	// the values for the metric will always be positive. The output is a
   179  	// `GAUGE` metric with `value_type` `DOUBLE`.
   180  	Aggregation_ALIGN_PERCENT_CHANGE Aggregation_Aligner = 23
   181  )
   182  
   183  // Enum value maps for Aggregation_Aligner.
   184  var (
   185  	Aggregation_Aligner_name = map[int32]string{
   186  		0:  "ALIGN_NONE",
   187  		1:  "ALIGN_DELTA",
   188  		2:  "ALIGN_RATE",
   189  		3:  "ALIGN_INTERPOLATE",
   190  		4:  "ALIGN_NEXT_OLDER",
   191  		10: "ALIGN_MIN",
   192  		11: "ALIGN_MAX",
   193  		12: "ALIGN_MEAN",
   194  		13: "ALIGN_COUNT",
   195  		14: "ALIGN_SUM",
   196  		15: "ALIGN_STDDEV",
   197  		16: "ALIGN_COUNT_TRUE",
   198  		24: "ALIGN_COUNT_FALSE",
   199  		17: "ALIGN_FRACTION_TRUE",
   200  		18: "ALIGN_PERCENTILE_99",
   201  		19: "ALIGN_PERCENTILE_95",
   202  		20: "ALIGN_PERCENTILE_50",
   203  		21: "ALIGN_PERCENTILE_05",
   204  		23: "ALIGN_PERCENT_CHANGE",
   205  	}
   206  	Aggregation_Aligner_value = map[string]int32{
   207  		"ALIGN_NONE":           0,
   208  		"ALIGN_DELTA":          1,
   209  		"ALIGN_RATE":           2,
   210  		"ALIGN_INTERPOLATE":    3,
   211  		"ALIGN_NEXT_OLDER":     4,
   212  		"ALIGN_MIN":            10,
   213  		"ALIGN_MAX":            11,
   214  		"ALIGN_MEAN":           12,
   215  		"ALIGN_COUNT":          13,
   216  		"ALIGN_SUM":            14,
   217  		"ALIGN_STDDEV":         15,
   218  		"ALIGN_COUNT_TRUE":     16,
   219  		"ALIGN_COUNT_FALSE":    24,
   220  		"ALIGN_FRACTION_TRUE":  17,
   221  		"ALIGN_PERCENTILE_99":  18,
   222  		"ALIGN_PERCENTILE_95":  19,
   223  		"ALIGN_PERCENTILE_50":  20,
   224  		"ALIGN_PERCENTILE_05":  21,
   225  		"ALIGN_PERCENT_CHANGE": 23,
   226  	}
   227  )
   228  
   229  func (x Aggregation_Aligner) Enum() *Aggregation_Aligner {
   230  	p := new(Aggregation_Aligner)
   231  	*p = x
   232  	return p
   233  }
   234  
   235  func (x Aggregation_Aligner) String() string {
   236  	return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
   237  }
   238  
   239  func (Aggregation_Aligner) Descriptor() protoreflect.EnumDescriptor {
   240  	return file_google_monitoring_dashboard_v1_common_proto_enumTypes[0].Descriptor()
   241  }
   242  
   243  func (Aggregation_Aligner) Type() protoreflect.EnumType {
   244  	return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[0]
   245  }
   246  
   247  func (x Aggregation_Aligner) Number() protoreflect.EnumNumber {
   248  	return protoreflect.EnumNumber(x)
   249  }
   250  
   251  // Deprecated: Use Aggregation_Aligner.Descriptor instead.
   252  func (Aggregation_Aligner) EnumDescriptor() ([]byte, []int) {
   253  	return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{0, 0}
   254  }
   255  
   256  // A Reducer operation describes how to aggregate data points from multiple
   257  // time series into a single time series, where the value of each data point
   258  // in the resulting series is a function of all the already aligned values in
   259  // the input time series.
   260  type Aggregation_Reducer int32
   261  
   262  const (
   263  	// No cross-time series reduction. The output of the `Aligner` is
   264  	// returned.
   265  	Aggregation_REDUCE_NONE Aggregation_Reducer = 0
   266  	// Reduce by computing the mean value across time series for each
   267  	// alignment period. This reducer is valid for
   268  	// [DELTA][google.api.MetricDescriptor.MetricKind.DELTA] and
   269  	// [GAUGE][google.api.MetricDescriptor.MetricKind.GAUGE] metrics with
   270  	// numeric or distribution values. The `value_type` of the output is
   271  	// [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
   272  	Aggregation_REDUCE_MEAN Aggregation_Reducer = 1
   273  	// Reduce by computing the minimum value across time series for each
   274  	// alignment period. This reducer is valid for `DELTA` and `GAUGE` metrics
   275  	// with numeric values. The `value_type` of the output is the same as the
   276  	// `value_type` of the input.
   277  	Aggregation_REDUCE_MIN Aggregation_Reducer = 2
   278  	// Reduce by computing the maximum value across time series for each
   279  	// alignment period. This reducer is valid for `DELTA` and `GAUGE` metrics
   280  	// with numeric values. The `value_type` of the output is the same as the
   281  	// `value_type` of the input.
   282  	Aggregation_REDUCE_MAX Aggregation_Reducer = 3
   283  	// Reduce by computing the sum across time series for each
   284  	// alignment period. This reducer is valid for `DELTA` and `GAUGE` metrics
   285  	// with numeric and distribution values. The `value_type` of the output is
   286  	// the same as the `value_type` of the input.
   287  	Aggregation_REDUCE_SUM Aggregation_Reducer = 4
   288  	// Reduce by computing the standard deviation across time series
   289  	// for each alignment period. This reducer is valid for `DELTA` and
   290  	// `GAUGE` metrics with numeric or distribution values. The `value_type`
   291  	// of the output is `DOUBLE`.
   292  	Aggregation_REDUCE_STDDEV Aggregation_Reducer = 5
   293  	// Reduce by computing the number of data points across time series
   294  	// for each alignment period. This reducer is valid for `DELTA` and
   295  	// `GAUGE` metrics of numeric, Boolean, distribution, and string
   296  	// `value_type`. The `value_type` of the output is `INT64`.
   297  	Aggregation_REDUCE_COUNT Aggregation_Reducer = 6
   298  	// Reduce by computing the number of `True`-valued data points across time
   299  	// series for each alignment period. This reducer is valid for `DELTA` and
   300  	// `GAUGE` metrics of Boolean `value_type`. The `value_type` of the output
   301  	// is `INT64`.
   302  	Aggregation_REDUCE_COUNT_TRUE Aggregation_Reducer = 7
   303  	// Reduce by computing the number of `False`-valued data points across time
   304  	// series for each alignment period. This reducer is valid for `DELTA` and
   305  	// `GAUGE` metrics of Boolean `value_type`. The `value_type` of the output
   306  	// is `INT64`.
   307  	Aggregation_REDUCE_COUNT_FALSE Aggregation_Reducer = 15
   308  	// Reduce by computing the ratio of the number of `True`-valued data points
   309  	// to the total number of data points for each alignment period. This
   310  	// reducer is valid for `DELTA` and `GAUGE` metrics of Boolean `value_type`.
   311  	// The output value is in the range [0.0, 1.0] and has `value_type`
   312  	// `DOUBLE`.
   313  	Aggregation_REDUCE_FRACTION_TRUE Aggregation_Reducer = 8
   314  	// Reduce by computing the [99th
   315  	// percentile](https://en.wikipedia.org/wiki/Percentile) of data points
   316  	// across time series for each alignment period. This reducer is valid for
   317  	// `GAUGE` and `DELTA` metrics of numeric and distribution type. The value
   318  	// of the output is `DOUBLE`.
   319  	Aggregation_REDUCE_PERCENTILE_99 Aggregation_Reducer = 9
   320  	// Reduce by computing the [95th
   321  	// percentile](https://en.wikipedia.org/wiki/Percentile) of data points
   322  	// across time series for each alignment period. This reducer is valid for
   323  	// `GAUGE` and `DELTA` metrics of numeric and distribution type. The value
   324  	// of the output is `DOUBLE`.
   325  	Aggregation_REDUCE_PERCENTILE_95 Aggregation_Reducer = 10
   326  	// Reduce by computing the [50th
   327  	// percentile](https://en.wikipedia.org/wiki/Percentile) of data points
   328  	// across time series for each alignment period. This reducer is valid for
   329  	// `GAUGE` and `DELTA` metrics of numeric and distribution type. The value
   330  	// of the output is `DOUBLE`.
   331  	Aggregation_REDUCE_PERCENTILE_50 Aggregation_Reducer = 11
   332  	// Reduce by computing the [5th
   333  	// percentile](https://en.wikipedia.org/wiki/Percentile) of data points
   334  	// across time series for each alignment period. This reducer is valid for
   335  	// `GAUGE` and `DELTA` metrics of numeric and distribution type. The value
   336  	// of the output is `DOUBLE`.
   337  	Aggregation_REDUCE_PERCENTILE_05 Aggregation_Reducer = 12
   338  )
   339  
   340  // Enum value maps for Aggregation_Reducer.
   341  var (
   342  	Aggregation_Reducer_name = map[int32]string{
   343  		0:  "REDUCE_NONE",
   344  		1:  "REDUCE_MEAN",
   345  		2:  "REDUCE_MIN",
   346  		3:  "REDUCE_MAX",
   347  		4:  "REDUCE_SUM",
   348  		5:  "REDUCE_STDDEV",
   349  		6:  "REDUCE_COUNT",
   350  		7:  "REDUCE_COUNT_TRUE",
   351  		15: "REDUCE_COUNT_FALSE",
   352  		8:  "REDUCE_FRACTION_TRUE",
   353  		9:  "REDUCE_PERCENTILE_99",
   354  		10: "REDUCE_PERCENTILE_95",
   355  		11: "REDUCE_PERCENTILE_50",
   356  		12: "REDUCE_PERCENTILE_05",
   357  	}
   358  	Aggregation_Reducer_value = map[string]int32{
   359  		"REDUCE_NONE":          0,
   360  		"REDUCE_MEAN":          1,
   361  		"REDUCE_MIN":           2,
   362  		"REDUCE_MAX":           3,
   363  		"REDUCE_SUM":           4,
   364  		"REDUCE_STDDEV":        5,
   365  		"REDUCE_COUNT":         6,
   366  		"REDUCE_COUNT_TRUE":    7,
   367  		"REDUCE_COUNT_FALSE":   15,
   368  		"REDUCE_FRACTION_TRUE": 8,
   369  		"REDUCE_PERCENTILE_99": 9,
   370  		"REDUCE_PERCENTILE_95": 10,
   371  		"REDUCE_PERCENTILE_50": 11,
   372  		"REDUCE_PERCENTILE_05": 12,
   373  	}
   374  )
   375  
   376  func (x Aggregation_Reducer) Enum() *Aggregation_Reducer {
   377  	p := new(Aggregation_Reducer)
   378  	*p = x
   379  	return p
   380  }
   381  
   382  func (x Aggregation_Reducer) String() string {
   383  	return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
   384  }
   385  
   386  func (Aggregation_Reducer) Descriptor() protoreflect.EnumDescriptor {
   387  	return file_google_monitoring_dashboard_v1_common_proto_enumTypes[1].Descriptor()
   388  }
   389  
   390  func (Aggregation_Reducer) Type() protoreflect.EnumType {
   391  	return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[1]
   392  }
   393  
   394  func (x Aggregation_Reducer) Number() protoreflect.EnumNumber {
   395  	return protoreflect.EnumNumber(x)
   396  }
   397  
   398  // Deprecated: Use Aggregation_Reducer.Descriptor instead.
   399  func (Aggregation_Reducer) EnumDescriptor() ([]byte, []int) {
   400  	return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{0, 1}
   401  }
   402  
   403  // The value reducers that can be applied to a `PickTimeSeriesFilter`.
   404  type PickTimeSeriesFilter_Method int32
   405  
   406  const (
   407  	// Not allowed. You must specify a different `Method` if you specify a
   408  	// `PickTimeSeriesFilter`.
   409  	PickTimeSeriesFilter_METHOD_UNSPECIFIED PickTimeSeriesFilter_Method = 0
   410  	// Select the mean of all values.
   411  	PickTimeSeriesFilter_METHOD_MEAN PickTimeSeriesFilter_Method = 1
   412  	// Select the maximum value.
   413  	PickTimeSeriesFilter_METHOD_MAX PickTimeSeriesFilter_Method = 2
   414  	// Select the minimum value.
   415  	PickTimeSeriesFilter_METHOD_MIN PickTimeSeriesFilter_Method = 3
   416  	// Compute the sum of all values.
   417  	PickTimeSeriesFilter_METHOD_SUM PickTimeSeriesFilter_Method = 4
   418  	// Select the most recent value.
   419  	PickTimeSeriesFilter_METHOD_LATEST PickTimeSeriesFilter_Method = 5
   420  )
   421  
   422  // Enum value maps for PickTimeSeriesFilter_Method.
   423  var (
   424  	PickTimeSeriesFilter_Method_name = map[int32]string{
   425  		0: "METHOD_UNSPECIFIED",
   426  		1: "METHOD_MEAN",
   427  		2: "METHOD_MAX",
   428  		3: "METHOD_MIN",
   429  		4: "METHOD_SUM",
   430  		5: "METHOD_LATEST",
   431  	}
   432  	PickTimeSeriesFilter_Method_value = map[string]int32{
   433  		"METHOD_UNSPECIFIED": 0,
   434  		"METHOD_MEAN":        1,
   435  		"METHOD_MAX":         2,
   436  		"METHOD_MIN":         3,
   437  		"METHOD_SUM":         4,
   438  		"METHOD_LATEST":      5,
   439  	}
   440  )
   441  
   442  func (x PickTimeSeriesFilter_Method) Enum() *PickTimeSeriesFilter_Method {
   443  	p := new(PickTimeSeriesFilter_Method)
   444  	*p = x
   445  	return p
   446  }
   447  
   448  func (x PickTimeSeriesFilter_Method) String() string {
   449  	return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
   450  }
   451  
   452  func (PickTimeSeriesFilter_Method) Descriptor() protoreflect.EnumDescriptor {
   453  	return file_google_monitoring_dashboard_v1_common_proto_enumTypes[2].Descriptor()
   454  }
   455  
   456  func (PickTimeSeriesFilter_Method) Type() protoreflect.EnumType {
   457  	return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[2]
   458  }
   459  
   460  func (x PickTimeSeriesFilter_Method) Number() protoreflect.EnumNumber {
   461  	return protoreflect.EnumNumber(x)
   462  }
   463  
   464  // Deprecated: Use PickTimeSeriesFilter_Method.Descriptor instead.
   465  func (PickTimeSeriesFilter_Method) EnumDescriptor() ([]byte, []int) {
   466  	return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{1, 0}
   467  }
   468  
   469  // Describes the ranking directions.
   470  type PickTimeSeriesFilter_Direction int32
   471  
   472  const (
   473  	// Not allowed. You must specify a different `Direction` if you specify a
   474  	// `PickTimeSeriesFilter`.
   475  	PickTimeSeriesFilter_DIRECTION_UNSPECIFIED PickTimeSeriesFilter_Direction = 0
   476  	// Pass the highest `num_time_series` ranking inputs.
   477  	PickTimeSeriesFilter_TOP PickTimeSeriesFilter_Direction = 1
   478  	// Pass the lowest `num_time_series` ranking inputs.
   479  	PickTimeSeriesFilter_BOTTOM PickTimeSeriesFilter_Direction = 2
   480  )
   481  
   482  // Enum value maps for PickTimeSeriesFilter_Direction.
   483  var (
   484  	PickTimeSeriesFilter_Direction_name = map[int32]string{
   485  		0: "DIRECTION_UNSPECIFIED",
   486  		1: "TOP",
   487  		2: "BOTTOM",
   488  	}
   489  	PickTimeSeriesFilter_Direction_value = map[string]int32{
   490  		"DIRECTION_UNSPECIFIED": 0,
   491  		"TOP":                   1,
   492  		"BOTTOM":                2,
   493  	}
   494  )
   495  
   496  func (x PickTimeSeriesFilter_Direction) Enum() *PickTimeSeriesFilter_Direction {
   497  	p := new(PickTimeSeriesFilter_Direction)
   498  	*p = x
   499  	return p
   500  }
   501  
   502  func (x PickTimeSeriesFilter_Direction) String() string {
   503  	return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
   504  }
   505  
   506  func (PickTimeSeriesFilter_Direction) Descriptor() protoreflect.EnumDescriptor {
   507  	return file_google_monitoring_dashboard_v1_common_proto_enumTypes[3].Descriptor()
   508  }
   509  
   510  func (PickTimeSeriesFilter_Direction) Type() protoreflect.EnumType {
   511  	return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[3]
   512  }
   513  
   514  func (x PickTimeSeriesFilter_Direction) Number() protoreflect.EnumNumber {
   515  	return protoreflect.EnumNumber(x)
   516  }
   517  
   518  // Deprecated: Use PickTimeSeriesFilter_Direction.Descriptor instead.
   519  func (PickTimeSeriesFilter_Direction) EnumDescriptor() ([]byte, []int) {
   520  	return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{1, 1}
   521  }
   522  
   523  // The filter methods that can be applied to a stream.
   524  type StatisticalTimeSeriesFilter_Method int32
   525  
   526  const (
   527  	// Not allowed in well-formed requests.
   528  	StatisticalTimeSeriesFilter_METHOD_UNSPECIFIED StatisticalTimeSeriesFilter_Method = 0
   529  	// Compute the outlier score of each stream.
   530  	StatisticalTimeSeriesFilter_METHOD_CLUSTER_OUTLIER StatisticalTimeSeriesFilter_Method = 1
   531  )
   532  
   533  // Enum value maps for StatisticalTimeSeriesFilter_Method.
   534  var (
   535  	StatisticalTimeSeriesFilter_Method_name = map[int32]string{
   536  		0: "METHOD_UNSPECIFIED",
   537  		1: "METHOD_CLUSTER_OUTLIER",
   538  	}
   539  	StatisticalTimeSeriesFilter_Method_value = map[string]int32{
   540  		"METHOD_UNSPECIFIED":     0,
   541  		"METHOD_CLUSTER_OUTLIER": 1,
   542  	}
   543  )
   544  
   545  func (x StatisticalTimeSeriesFilter_Method) Enum() *StatisticalTimeSeriesFilter_Method {
   546  	p := new(StatisticalTimeSeriesFilter_Method)
   547  	*p = x
   548  	return p
   549  }
   550  
   551  func (x StatisticalTimeSeriesFilter_Method) String() string {
   552  	return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
   553  }
   554  
   555  func (StatisticalTimeSeriesFilter_Method) Descriptor() protoreflect.EnumDescriptor {
   556  	return file_google_monitoring_dashboard_v1_common_proto_enumTypes[4].Descriptor()
   557  }
   558  
   559  func (StatisticalTimeSeriesFilter_Method) Type() protoreflect.EnumType {
   560  	return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[4]
   561  }
   562  
   563  func (x StatisticalTimeSeriesFilter_Method) Number() protoreflect.EnumNumber {
   564  	return protoreflect.EnumNumber(x)
   565  }
   566  
   567  // Deprecated: Use StatisticalTimeSeriesFilter_Method.Descriptor instead.
   568  func (StatisticalTimeSeriesFilter_Method) EnumDescriptor() ([]byte, []int) {
   569  	return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{2, 0}
   570  }
   571  
   572  // Describes how to combine multiple time series to provide a different view of
   573  // the data.  Aggregation of time series is done in two steps. First, each time
   574  // series in the set is _aligned_ to the same time interval boundaries, then the
   575  // set of time series is optionally _reduced_ in number.
   576  //
   577  // Alignment consists of applying the `per_series_aligner` operation
   578  // to each time series after its data has been divided into regular
   579  // `alignment_period` time intervals. This process takes _all_ of the data
   580  // points in an alignment period, applies a mathematical transformation such as
   581  // averaging, minimum, maximum, delta, etc., and converts them into a single
   582  // data point per period.
   583  //
   584  // Reduction is when the aligned and transformed time series can optionally be
   585  // combined, reducing the number of time series through similar mathematical
   586  // transformations. Reduction involves applying a `cross_series_reducer` to
   587  // all the time series, optionally sorting the time series into subsets with
   588  // `group_by_fields`, and applying the reducer to each subset.
   589  //
   590  // The raw time series data can contain a huge amount of information from
   591  // multiple sources. Alignment and reduction transforms this mass of data into
   592  // a more manageable and representative collection of data, for example "the
   593  // 95% latency across the average of all tasks in a cluster". This
   594  // representative data can be more easily graphed and comprehended, and the
   595  // individual time series data is still available for later drilldown. For more
   596  // details, see [Filtering and
   597  // aggregation](https://cloud.google.com/monitoring/api/v3/aggregation).
   598  type Aggregation struct {
   599  	state         protoimpl.MessageState
   600  	sizeCache     protoimpl.SizeCache
   601  	unknownFields protoimpl.UnknownFields
   602  
   603  	// The `alignment_period` specifies a time interval, in seconds, that is used
   604  	// to divide the data in all the
   605  	// [time series][google.monitoring.v3.TimeSeries] into consistent blocks of
   606  	// time. This will be done before the per-series aligner can be applied to
   607  	// the data.
   608  	//
   609  	// The value must be at least 60 seconds. If a per-series aligner other than
   610  	// `ALIGN_NONE` is specified, this field is required or an error is returned.
   611  	// If no per-series aligner is specified, or the aligner `ALIGN_NONE` is
   612  	// specified, then this field is ignored.
   613  	//
   614  	// The maximum value of the `alignment_period` is 2 years, or 104 weeks.
   615  	AlignmentPeriod *durationpb.Duration `protobuf:"bytes,1,opt,name=alignment_period,json=alignmentPeriod,proto3" json:"alignment_period,omitempty"`
   616  	// An `Aligner` describes how to bring the data points in a single
   617  	// time series into temporal alignment. Except for `ALIGN_NONE`, all
   618  	// alignments cause all the data points in an `alignment_period` to be
   619  	// mathematically grouped together, resulting in a single data point for
   620  	// each `alignment_period` with end timestamp at the end of the period.
   621  	//
   622  	// Not all alignment operations may be applied to all time series. The valid
   623  	// choices depend on the `metric_kind` and `value_type` of the original time
   624  	// series. Alignment can change the `metric_kind` or the `value_type` of
   625  	// the time series.
   626  	//
   627  	// Time series data must be aligned in order to perform cross-time
   628  	// series reduction. If `cross_series_reducer` is specified, then
   629  	// `per_series_aligner` must be specified and not equal to `ALIGN_NONE`
   630  	// and `alignment_period` must be specified; otherwise, an error is
   631  	// returned.
   632  	PerSeriesAligner Aggregation_Aligner `protobuf:"varint,2,opt,name=per_series_aligner,json=perSeriesAligner,proto3,enum=google.monitoring.dashboard.v1.Aggregation_Aligner" json:"per_series_aligner,omitempty"`
   633  	// The reduction operation to be used to combine time series into a single
   634  	// time series, where the value of each data point in the resulting series is
   635  	// a function of all the already aligned values in the input time series.
   636  	//
   637  	// Not all reducer operations can be applied to all time series. The valid
   638  	// choices depend on the `metric_kind` and the `value_type` of the original
   639  	// time series. Reduction can yield a time series with a different
   640  	// `metric_kind` or `value_type` than the input time series.
   641  	//
   642  	// Time series data must first be aligned (see `per_series_aligner`) in order
   643  	// to perform cross-time series reduction. If `cross_series_reducer` is
   644  	// specified, then `per_series_aligner` must be specified, and must not be
   645  	// `ALIGN_NONE`. An `alignment_period` must also be specified; otherwise, an
   646  	// error is returned.
   647  	CrossSeriesReducer Aggregation_Reducer `protobuf:"varint,4,opt,name=cross_series_reducer,json=crossSeriesReducer,proto3,enum=google.monitoring.dashboard.v1.Aggregation_Reducer" json:"cross_series_reducer,omitempty"`
   648  	// The set of fields to preserve when `cross_series_reducer` is
   649  	// specified. The `group_by_fields` determine how the time series are
   650  	// partitioned into subsets prior to applying the aggregation
   651  	// operation. Each subset contains time series that have the same
   652  	// value for each of the grouping fields. Each individual time
   653  	// series is a member of exactly one subset. The
   654  	// `cross_series_reducer` is applied to each subset of time series.
   655  	// It is not possible to reduce across different resource types, so
   656  	// this field implicitly contains `resource.type`.  Fields not
   657  	// specified in `group_by_fields` are aggregated away.  If
   658  	// `group_by_fields` is not specified and all the time series have
   659  	// the same resource type, then the time series are aggregated into
   660  	// a single output time series. If `cross_series_reducer` is not
   661  	// defined, this field is ignored.
   662  	GroupByFields []string `protobuf:"bytes,5,rep,name=group_by_fields,json=groupByFields,proto3" json:"group_by_fields,omitempty"`
   663  }
   664  
   665  func (x *Aggregation) Reset() {
   666  	*x = Aggregation{}
   667  	if protoimpl.UnsafeEnabled {
   668  		mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[0]
   669  		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
   670  		ms.StoreMessageInfo(mi)
   671  	}
   672  }
   673  
   674  func (x *Aggregation) String() string {
   675  	return protoimpl.X.MessageStringOf(x)
   676  }
   677  
   678  func (*Aggregation) ProtoMessage() {}
   679  
   680  func (x *Aggregation) ProtoReflect() protoreflect.Message {
   681  	mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[0]
   682  	if protoimpl.UnsafeEnabled && x != nil {
   683  		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
   684  		if ms.LoadMessageInfo() == nil {
   685  			ms.StoreMessageInfo(mi)
   686  		}
   687  		return ms
   688  	}
   689  	return mi.MessageOf(x)
   690  }
   691  
   692  // Deprecated: Use Aggregation.ProtoReflect.Descriptor instead.
   693  func (*Aggregation) Descriptor() ([]byte, []int) {
   694  	return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{0}
   695  }
   696  
   697  func (x *Aggregation) GetAlignmentPeriod() *durationpb.Duration {
   698  	if x != nil {
   699  		return x.AlignmentPeriod
   700  	}
   701  	return nil
   702  }
   703  
   704  func (x *Aggregation) GetPerSeriesAligner() Aggregation_Aligner {
   705  	if x != nil {
   706  		return x.PerSeriesAligner
   707  	}
   708  	return Aggregation_ALIGN_NONE
   709  }
   710  
   711  func (x *Aggregation) GetCrossSeriesReducer() Aggregation_Reducer {
   712  	if x != nil {
   713  		return x.CrossSeriesReducer
   714  	}
   715  	return Aggregation_REDUCE_NONE
   716  }
   717  
   718  func (x *Aggregation) GetGroupByFields() []string {
   719  	if x != nil {
   720  		return x.GroupByFields
   721  	}
   722  	return nil
   723  }
   724  
   725  // Describes a ranking-based time series filter. Each input time series is
   726  // ranked with an aligner. The filter will allow up to `num_time_series` time
   727  // series to pass through it, selecting them based on the relative ranking.
   728  //
   729  // For example, if `ranking_method` is `METHOD_MEAN`,`direction` is `BOTTOM`,
   730  // and `num_time_series` is 3, then the 3 times series with the lowest mean
   731  // values will pass through the filter.
   732  type PickTimeSeriesFilter struct {
   733  	state         protoimpl.MessageState
   734  	sizeCache     protoimpl.SizeCache
   735  	unknownFields protoimpl.UnknownFields
   736  
   737  	// `ranking_method` is applied to each time series independently to produce
   738  	// the value which will be used to compare the time series to other time
   739  	// series.
   740  	RankingMethod PickTimeSeriesFilter_Method `protobuf:"varint,1,opt,name=ranking_method,json=rankingMethod,proto3,enum=google.monitoring.dashboard.v1.PickTimeSeriesFilter_Method" json:"ranking_method,omitempty"`
   741  	// How many time series to allow to pass through the filter.
   742  	NumTimeSeries int32 `protobuf:"varint,2,opt,name=num_time_series,json=numTimeSeries,proto3" json:"num_time_series,omitempty"`
   743  	// How to use the ranking to select time series that pass through the filter.
   744  	Direction PickTimeSeriesFilter_Direction `protobuf:"varint,3,opt,name=direction,proto3,enum=google.monitoring.dashboard.v1.PickTimeSeriesFilter_Direction" json:"direction,omitempty"`
   745  	// Select the top N streams/time series within this time interval
   746  	Interval *interval.Interval `protobuf:"bytes,4,opt,name=interval,proto3" json:"interval,omitempty"`
   747  }
   748  
   749  func (x *PickTimeSeriesFilter) Reset() {
   750  	*x = PickTimeSeriesFilter{}
   751  	if protoimpl.UnsafeEnabled {
   752  		mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[1]
   753  		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
   754  		ms.StoreMessageInfo(mi)
   755  	}
   756  }
   757  
   758  func (x *PickTimeSeriesFilter) String() string {
   759  	return protoimpl.X.MessageStringOf(x)
   760  }
   761  
   762  func (*PickTimeSeriesFilter) ProtoMessage() {}
   763  
   764  func (x *PickTimeSeriesFilter) ProtoReflect() protoreflect.Message {
   765  	mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[1]
   766  	if protoimpl.UnsafeEnabled && x != nil {
   767  		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
   768  		if ms.LoadMessageInfo() == nil {
   769  			ms.StoreMessageInfo(mi)
   770  		}
   771  		return ms
   772  	}
   773  	return mi.MessageOf(x)
   774  }
   775  
   776  // Deprecated: Use PickTimeSeriesFilter.ProtoReflect.Descriptor instead.
   777  func (*PickTimeSeriesFilter) Descriptor() ([]byte, []int) {
   778  	return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{1}
   779  }
   780  
   781  func (x *PickTimeSeriesFilter) GetRankingMethod() PickTimeSeriesFilter_Method {
   782  	if x != nil {
   783  		return x.RankingMethod
   784  	}
   785  	return PickTimeSeriesFilter_METHOD_UNSPECIFIED
   786  }
   787  
   788  func (x *PickTimeSeriesFilter) GetNumTimeSeries() int32 {
   789  	if x != nil {
   790  		return x.NumTimeSeries
   791  	}
   792  	return 0
   793  }
   794  
   795  func (x *PickTimeSeriesFilter) GetDirection() PickTimeSeriesFilter_Direction {
   796  	if x != nil {
   797  		return x.Direction
   798  	}
   799  	return PickTimeSeriesFilter_DIRECTION_UNSPECIFIED
   800  }
   801  
   802  func (x *PickTimeSeriesFilter) GetInterval() *interval.Interval {
   803  	if x != nil {
   804  		return x.Interval
   805  	}
   806  	return nil
   807  }
   808  
   809  // A filter that ranks streams based on their statistical relation to other
   810  // streams in a request.
   811  // Note: This field is deprecated and completely ignored by the API.
   812  type StatisticalTimeSeriesFilter struct {
   813  	state         protoimpl.MessageState
   814  	sizeCache     protoimpl.SizeCache
   815  	unknownFields protoimpl.UnknownFields
   816  
   817  	// `rankingMethod` is applied to a set of time series, and then the produced
   818  	// value for each individual time series is used to compare a given time
   819  	// series to others.
   820  	// These are methods that cannot be applied stream-by-stream, but rather
   821  	// require the full context of a request to evaluate time series.
   822  	RankingMethod StatisticalTimeSeriesFilter_Method `protobuf:"varint,1,opt,name=ranking_method,json=rankingMethod,proto3,enum=google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter_Method" json:"ranking_method,omitempty"`
   823  	// How many time series to output.
   824  	NumTimeSeries int32 `protobuf:"varint,2,opt,name=num_time_series,json=numTimeSeries,proto3" json:"num_time_series,omitempty"`
   825  }
   826  
   827  func (x *StatisticalTimeSeriesFilter) Reset() {
   828  	*x = StatisticalTimeSeriesFilter{}
   829  	if protoimpl.UnsafeEnabled {
   830  		mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[2]
   831  		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
   832  		ms.StoreMessageInfo(mi)
   833  	}
   834  }
   835  
   836  func (x *StatisticalTimeSeriesFilter) String() string {
   837  	return protoimpl.X.MessageStringOf(x)
   838  }
   839  
   840  func (*StatisticalTimeSeriesFilter) ProtoMessage() {}
   841  
   842  func (x *StatisticalTimeSeriesFilter) ProtoReflect() protoreflect.Message {
   843  	mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[2]
   844  	if protoimpl.UnsafeEnabled && x != nil {
   845  		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
   846  		if ms.LoadMessageInfo() == nil {
   847  			ms.StoreMessageInfo(mi)
   848  		}
   849  		return ms
   850  	}
   851  	return mi.MessageOf(x)
   852  }
   853  
   854  // Deprecated: Use StatisticalTimeSeriesFilter.ProtoReflect.Descriptor instead.
   855  func (*StatisticalTimeSeriesFilter) Descriptor() ([]byte, []int) {
   856  	return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{2}
   857  }
   858  
   859  func (x *StatisticalTimeSeriesFilter) GetRankingMethod() StatisticalTimeSeriesFilter_Method {
   860  	if x != nil {
   861  		return x.RankingMethod
   862  	}
   863  	return StatisticalTimeSeriesFilter_METHOD_UNSPECIFIED
   864  }
   865  
   866  func (x *StatisticalTimeSeriesFilter) GetNumTimeSeries() int32 {
   867  	if x != nil {
   868  		return x.NumTimeSeries
   869  	}
   870  	return 0
   871  }
   872  
   873  var File_google_monitoring_dashboard_v1_common_proto protoreflect.FileDescriptor
   874  
   875  var file_google_monitoring_dashboard_v1_common_proto_rawDesc = []byte{
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  1009  	0x6f, 0x72, 0x69, 0x6e, 0x67, 0x3a, 0x3a, 0x44, 0x61, 0x73, 0x68, 0x62, 0x6f, 0x61, 0x72, 0x64,
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  1011  }
  1012  
  1013  var (
  1014  	file_google_monitoring_dashboard_v1_common_proto_rawDescOnce sync.Once
  1015  	file_google_monitoring_dashboard_v1_common_proto_rawDescData = file_google_monitoring_dashboard_v1_common_proto_rawDesc
  1016  )
  1017  
  1018  func file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP() []byte {
  1019  	file_google_monitoring_dashboard_v1_common_proto_rawDescOnce.Do(func() {
  1020  		file_google_monitoring_dashboard_v1_common_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_monitoring_dashboard_v1_common_proto_rawDescData)
  1021  	})
  1022  	return file_google_monitoring_dashboard_v1_common_proto_rawDescData
  1023  }
  1024  
  1025  var file_google_monitoring_dashboard_v1_common_proto_enumTypes = make([]protoimpl.EnumInfo, 5)
  1026  var file_google_monitoring_dashboard_v1_common_proto_msgTypes = make([]protoimpl.MessageInfo, 3)
  1027  var file_google_monitoring_dashboard_v1_common_proto_goTypes = []interface{}{
  1028  	(Aggregation_Aligner)(0),                // 0: google.monitoring.dashboard.v1.Aggregation.Aligner
  1029  	(Aggregation_Reducer)(0),                // 1: google.monitoring.dashboard.v1.Aggregation.Reducer
  1030  	(PickTimeSeriesFilter_Method)(0),        // 2: google.monitoring.dashboard.v1.PickTimeSeriesFilter.Method
  1031  	(PickTimeSeriesFilter_Direction)(0),     // 3: google.monitoring.dashboard.v1.PickTimeSeriesFilter.Direction
  1032  	(StatisticalTimeSeriesFilter_Method)(0), // 4: google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter.Method
  1033  	(*Aggregation)(nil),                     // 5: google.monitoring.dashboard.v1.Aggregation
  1034  	(*PickTimeSeriesFilter)(nil),            // 6: google.monitoring.dashboard.v1.PickTimeSeriesFilter
  1035  	(*StatisticalTimeSeriesFilter)(nil),     // 7: google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter
  1036  	(*durationpb.Duration)(nil),             // 8: google.protobuf.Duration
  1037  	(*interval.Interval)(nil),               // 9: google.type.Interval
  1038  }
  1039  var file_google_monitoring_dashboard_v1_common_proto_depIdxs = []int32{
  1040  	8, // 0: google.monitoring.dashboard.v1.Aggregation.alignment_period:type_name -> google.protobuf.Duration
  1041  	0, // 1: google.monitoring.dashboard.v1.Aggregation.per_series_aligner:type_name -> google.monitoring.dashboard.v1.Aggregation.Aligner
  1042  	1, // 2: google.monitoring.dashboard.v1.Aggregation.cross_series_reducer:type_name -> google.monitoring.dashboard.v1.Aggregation.Reducer
  1043  	2, // 3: google.monitoring.dashboard.v1.PickTimeSeriesFilter.ranking_method:type_name -> google.monitoring.dashboard.v1.PickTimeSeriesFilter.Method
  1044  	3, // 4: google.monitoring.dashboard.v1.PickTimeSeriesFilter.direction:type_name -> google.monitoring.dashboard.v1.PickTimeSeriesFilter.Direction
  1045  	9, // 5: google.monitoring.dashboard.v1.PickTimeSeriesFilter.interval:type_name -> google.type.Interval
  1046  	4, // 6: google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter.ranking_method:type_name -> google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter.Method
  1047  	7, // [7:7] is the sub-list for method output_type
  1048  	7, // [7:7] is the sub-list for method input_type
  1049  	7, // [7:7] is the sub-list for extension type_name
  1050  	7, // [7:7] is the sub-list for extension extendee
  1051  	0, // [0:7] is the sub-list for field type_name
  1052  }
  1053  
  1054  func init() { file_google_monitoring_dashboard_v1_common_proto_init() }
  1055  func file_google_monitoring_dashboard_v1_common_proto_init() {
  1056  	if File_google_monitoring_dashboard_v1_common_proto != nil {
  1057  		return
  1058  	}
  1059  	if !protoimpl.UnsafeEnabled {
  1060  		file_google_monitoring_dashboard_v1_common_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
  1061  			switch v := v.(*Aggregation); i {
  1062  			case 0:
  1063  				return &v.state
  1064  			case 1:
  1065  				return &v.sizeCache
  1066  			case 2:
  1067  				return &v.unknownFields
  1068  			default:
  1069  				return nil
  1070  			}
  1071  		}
  1072  		file_google_monitoring_dashboard_v1_common_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
  1073  			switch v := v.(*PickTimeSeriesFilter); i {
  1074  			case 0:
  1075  				return &v.state
  1076  			case 1:
  1077  				return &v.sizeCache
  1078  			case 2:
  1079  				return &v.unknownFields
  1080  			default:
  1081  				return nil
  1082  			}
  1083  		}
  1084  		file_google_monitoring_dashboard_v1_common_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
  1085  			switch v := v.(*StatisticalTimeSeriesFilter); i {
  1086  			case 0:
  1087  				return &v.state
  1088  			case 1:
  1089  				return &v.sizeCache
  1090  			case 2:
  1091  				return &v.unknownFields
  1092  			default:
  1093  				return nil
  1094  			}
  1095  		}
  1096  	}
  1097  	type x struct{}
  1098  	out := protoimpl.TypeBuilder{
  1099  		File: protoimpl.DescBuilder{
  1100  			GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
  1101  			RawDescriptor: file_google_monitoring_dashboard_v1_common_proto_rawDesc,
  1102  			NumEnums:      5,
  1103  			NumMessages:   3,
  1104  			NumExtensions: 0,
  1105  			NumServices:   0,
  1106  		},
  1107  		GoTypes:           file_google_monitoring_dashboard_v1_common_proto_goTypes,
  1108  		DependencyIndexes: file_google_monitoring_dashboard_v1_common_proto_depIdxs,
  1109  		EnumInfos:         file_google_monitoring_dashboard_v1_common_proto_enumTypes,
  1110  		MessageInfos:      file_google_monitoring_dashboard_v1_common_proto_msgTypes,
  1111  	}.Build()
  1112  	File_google_monitoring_dashboard_v1_common_proto = out.File
  1113  	file_google_monitoring_dashboard_v1_common_proto_rawDesc = nil
  1114  	file_google_monitoring_dashboard_v1_common_proto_goTypes = nil
  1115  	file_google_monitoring_dashboard_v1_common_proto_depIdxs = nil
  1116  }
  1117  

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