| // Copyright 2019 Google LLC. |
| // |
| // Licensed under the Apache License, Version 2.0 (the "License"); |
| // you may not use this file except in compliance with the License. |
| // You may obtain a copy of the License at |
| // |
| // http://www.apache.org/licenses/LICENSE-2.0 |
| // |
| // Unless required by applicable law or agreed to in writing, software |
| // distributed under the License is distributed on an "AS IS" BASIS, |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| // See the License for the specific language governing permissions and |
| // limitations under the License. |
| // |
| |
| // Code generated by protoc-gen-go. DO NOT EDIT. |
| // versions: |
| // protoc-gen-go v1.22.0 |
| // protoc v3.12.3 |
| // source: google/monitoring/dashboard/v1/common.proto |
| |
| package dashboard |
| |
| import ( |
| reflect "reflect" |
| sync "sync" |
| |
| proto "github.com/golang/protobuf/proto" |
| duration "github.com/golang/protobuf/ptypes/duration" |
| protoreflect "google.golang.org/protobuf/reflect/protoreflect" |
| protoimpl "google.golang.org/protobuf/runtime/protoimpl" |
| ) |
| |
| const ( |
| // Verify that this generated code is sufficiently up-to-date. |
| _ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion) |
| // Verify that runtime/protoimpl is sufficiently up-to-date. |
| _ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20) |
| ) |
| |
| // This is a compile-time assertion that a sufficiently up-to-date version |
| // of the legacy proto package is being used. |
| const _ = proto.ProtoPackageIsVersion4 |
| |
| // The Aligner describes how to bring the data points in a single |
| // time series into temporal alignment. |
| type Aggregation_Aligner int32 |
| |
| const ( |
| // 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. |
| Aggregation_ALIGN_NONE Aggregation_Aligner = 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. |
| // |
| // One can think of this aligner as a rate but without time units; that |
| // is, the output is conceptually (second_point - first_point). |
| Aggregation_ALIGN_DELTA Aggregation_Aligner = 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]. |
| // |
| // One can think of this aligner as conceptually providing the slope of |
| // the line that passes through the value at the start and end of the |
| // window. In other words, this is conceptually ((y1 - y0)/(t1 - t0)), |
| // and the output unit is one that has a "/time" dimension. |
| // |
| // If, by rate, you are looking for percentage change, see the |
| // `ALIGN_PERCENT_CHANGE` aligner option. |
| Aggregation_ALIGN_RATE Aggregation_Aligner = 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. |
| Aggregation_ALIGN_INTERPOLATE Aggregation_Aligner = 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. |
| Aggregation_ALIGN_NEXT_OLDER Aggregation_Aligner = 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. |
| Aggregation_ALIGN_MIN Aggregation_Aligner = 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. |
| Aggregation_ALIGN_MAX Aggregation_Aligner = 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]. |
| Aggregation_ALIGN_MEAN Aggregation_Aligner = 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]. |
| Aggregation_ALIGN_COUNT Aggregation_Aligner = 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. |
| Aggregation_ALIGN_SUM Aggregation_Aligner = 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]. |
| Aggregation_ALIGN_STDDEV Aggregation_Aligner = 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]. |
| Aggregation_ALIGN_COUNT_TRUE Aggregation_Aligner = 16 |
| // Align time series via aggregation. The resulting data point in |
| // the alignment period is the count of False-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]. |
| Aggregation_ALIGN_COUNT_FALSE Aggregation_Aligner = 24 |
| // 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]. |
| Aggregation_ALIGN_FRACTION_TRUE Aggregation_Aligner = 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]. |
| Aggregation_ALIGN_PERCENTILE_99 Aggregation_Aligner = 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]. |
| Aggregation_ALIGN_PERCENTILE_95 Aggregation_Aligner = 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]. |
| Aggregation_ALIGN_PERCENTILE_50 Aggregation_Aligner = 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]. |
| Aggregation_ALIGN_PERCENTILE_05 Aggregation_Aligner = 21 |
| // Align and convert to a percentage change. This alignment is valid for |
| // gauge and delta metrics with numeric values. This alignment conceptually |
| // computes the equivalent of "((current - previous)/previous)*100" |
| // where previous value is determined based on the alignmentPeriod. |
| // In the event that previous is 0 the calculated value is infinity with the |
| // exception that if both (current - previous) and previous are 0 the |
| // calculated value is 0. |
| // A 10 minute moving mean is computed at each point of the time window |
| // prior to the above calculation to smooth the metric and prevent false |
| // positives from very short lived spikes. |
| // Only applicable for data that is >= 0. Any values < 0 are treated as |
| // no data. While delta metrics are accepted by this alignment special care |
| // should be taken that the values for the metric will always be positive. |
| // The output is a gauge metric with value type |
| // [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE]. |
| Aggregation_ALIGN_PERCENT_CHANGE Aggregation_Aligner = 23 |
| ) |
| |
| // Enum value maps for Aggregation_Aligner. |
| var ( |
| Aggregation_Aligner_name = map[int32]string{ |
| 0: "ALIGN_NONE", |
| 1: "ALIGN_DELTA", |
| 2: "ALIGN_RATE", |
| 3: "ALIGN_INTERPOLATE", |
| 4: "ALIGN_NEXT_OLDER", |
| 10: "ALIGN_MIN", |
| 11: "ALIGN_MAX", |
| 12: "ALIGN_MEAN", |
| 13: "ALIGN_COUNT", |
| 14: "ALIGN_SUM", |
| 15: "ALIGN_STDDEV", |
| 16: "ALIGN_COUNT_TRUE", |
| 24: "ALIGN_COUNT_FALSE", |
| 17: "ALIGN_FRACTION_TRUE", |
| 18: "ALIGN_PERCENTILE_99", |
| 19: "ALIGN_PERCENTILE_95", |
| 20: "ALIGN_PERCENTILE_50", |
| 21: "ALIGN_PERCENTILE_05", |
| 23: "ALIGN_PERCENT_CHANGE", |
| } |
| Aggregation_Aligner_value = map[string]int32{ |
| "ALIGN_NONE": 0, |
| "ALIGN_DELTA": 1, |
| "ALIGN_RATE": 2, |
| "ALIGN_INTERPOLATE": 3, |
| "ALIGN_NEXT_OLDER": 4, |
| "ALIGN_MIN": 10, |
| "ALIGN_MAX": 11, |
| "ALIGN_MEAN": 12, |
| "ALIGN_COUNT": 13, |
| "ALIGN_SUM": 14, |
| "ALIGN_STDDEV": 15, |
| "ALIGN_COUNT_TRUE": 16, |
| "ALIGN_COUNT_FALSE": 24, |
| "ALIGN_FRACTION_TRUE": 17, |
| "ALIGN_PERCENTILE_99": 18, |
| "ALIGN_PERCENTILE_95": 19, |
| "ALIGN_PERCENTILE_50": 20, |
| "ALIGN_PERCENTILE_05": 21, |
| "ALIGN_PERCENT_CHANGE": 23, |
| } |
| ) |
| |
| func (x Aggregation_Aligner) Enum() *Aggregation_Aligner { |
| p := new(Aggregation_Aligner) |
| *p = x |
| return p |
| } |
| |
| func (x Aggregation_Aligner) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (Aggregation_Aligner) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_monitoring_dashboard_v1_common_proto_enumTypes[0].Descriptor() |
| } |
| |
| func (Aggregation_Aligner) Type() protoreflect.EnumType { |
| return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[0] |
| } |
| |
| func (x Aggregation_Aligner) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use Aggregation_Aligner.Descriptor instead. |
| func (Aggregation_Aligner) EnumDescriptor() ([]byte, []int) { |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{0, 0} |
| } |
| |
| // A Reducer describes how to aggregate data points from multiple |
| // time series into a single time series. |
| type Aggregation_Reducer int32 |
| |
| const ( |
| // No cross-time series reduction. The output of the aligner is |
| // returned. |
| Aggregation_REDUCE_NONE Aggregation_Reducer = 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]. |
| Aggregation_REDUCE_MEAN Aggregation_Reducer = 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. |
| Aggregation_REDUCE_MIN Aggregation_Reducer = 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. |
| Aggregation_REDUCE_MAX Aggregation_Reducer = 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. |
| Aggregation_REDUCE_SUM Aggregation_Reducer = 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]. |
| Aggregation_REDUCE_STDDEV Aggregation_Reducer = 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]. |
| Aggregation_REDUCE_COUNT Aggregation_Reducer = 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]. |
| Aggregation_REDUCE_COUNT_TRUE Aggregation_Reducer = 7 |
| // Reduce by computing the count of False-valued data points across time |
| // series for each alignment period. This reducer is valid for delta |
| // and gauge metrics of Boolean value type. The value type of |
| // the output is [INT64][google.api.MetricDescriptor.ValueType.INT64]. |
| Aggregation_REDUCE_COUNT_FALSE Aggregation_Reducer = 15 |
| // 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]. |
| Aggregation_REDUCE_FRACTION_TRUE Aggregation_Reducer = 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] |
| Aggregation_REDUCE_PERCENTILE_99 Aggregation_Reducer = 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] |
| Aggregation_REDUCE_PERCENTILE_95 Aggregation_Reducer = 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] |
| Aggregation_REDUCE_PERCENTILE_50 Aggregation_Reducer = 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] |
| Aggregation_REDUCE_PERCENTILE_05 Aggregation_Reducer = 12 |
| ) |
| |
| // Enum value maps for Aggregation_Reducer. |
| var ( |
| Aggregation_Reducer_name = map[int32]string{ |
| 0: "REDUCE_NONE", |
| 1: "REDUCE_MEAN", |
| 2: "REDUCE_MIN", |
| 3: "REDUCE_MAX", |
| 4: "REDUCE_SUM", |
| 5: "REDUCE_STDDEV", |
| 6: "REDUCE_COUNT", |
| 7: "REDUCE_COUNT_TRUE", |
| 15: "REDUCE_COUNT_FALSE", |
| 8: "REDUCE_FRACTION_TRUE", |
| 9: "REDUCE_PERCENTILE_99", |
| 10: "REDUCE_PERCENTILE_95", |
| 11: "REDUCE_PERCENTILE_50", |
| 12: "REDUCE_PERCENTILE_05", |
| } |
| Aggregation_Reducer_value = map[string]int32{ |
| "REDUCE_NONE": 0, |
| "REDUCE_MEAN": 1, |
| "REDUCE_MIN": 2, |
| "REDUCE_MAX": 3, |
| "REDUCE_SUM": 4, |
| "REDUCE_STDDEV": 5, |
| "REDUCE_COUNT": 6, |
| "REDUCE_COUNT_TRUE": 7, |
| "REDUCE_COUNT_FALSE": 15, |
| "REDUCE_FRACTION_TRUE": 8, |
| "REDUCE_PERCENTILE_99": 9, |
| "REDUCE_PERCENTILE_95": 10, |
| "REDUCE_PERCENTILE_50": 11, |
| "REDUCE_PERCENTILE_05": 12, |
| } |
| ) |
| |
| func (x Aggregation_Reducer) Enum() *Aggregation_Reducer { |
| p := new(Aggregation_Reducer) |
| *p = x |
| return p |
| } |
| |
| func (x Aggregation_Reducer) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (Aggregation_Reducer) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_monitoring_dashboard_v1_common_proto_enumTypes[1].Descriptor() |
| } |
| |
| func (Aggregation_Reducer) Type() protoreflect.EnumType { |
| return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[1] |
| } |
| |
| func (x Aggregation_Reducer) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use Aggregation_Reducer.Descriptor instead. |
| func (Aggregation_Reducer) EnumDescriptor() ([]byte, []int) { |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{0, 1} |
| } |
| |
| // The value reducers that can be applied to a PickTimeSeriesFilter. |
| type PickTimeSeriesFilter_Method int32 |
| |
| const ( |
| // Not allowed in well-formed requests. |
| PickTimeSeriesFilter_METHOD_UNSPECIFIED PickTimeSeriesFilter_Method = 0 |
| // Select the mean of all values. |
| PickTimeSeriesFilter_METHOD_MEAN PickTimeSeriesFilter_Method = 1 |
| // Select the maximum value. |
| PickTimeSeriesFilter_METHOD_MAX PickTimeSeriesFilter_Method = 2 |
| // Select the minimum value. |
| PickTimeSeriesFilter_METHOD_MIN PickTimeSeriesFilter_Method = 3 |
| // Compute the sum of all values. |
| PickTimeSeriesFilter_METHOD_SUM PickTimeSeriesFilter_Method = 4 |
| // Select the most recent value. |
| PickTimeSeriesFilter_METHOD_LATEST PickTimeSeriesFilter_Method = 5 |
| ) |
| |
| // Enum value maps for PickTimeSeriesFilter_Method. |
| var ( |
| PickTimeSeriesFilter_Method_name = map[int32]string{ |
| 0: "METHOD_UNSPECIFIED", |
| 1: "METHOD_MEAN", |
| 2: "METHOD_MAX", |
| 3: "METHOD_MIN", |
| 4: "METHOD_SUM", |
| 5: "METHOD_LATEST", |
| } |
| PickTimeSeriesFilter_Method_value = map[string]int32{ |
| "METHOD_UNSPECIFIED": 0, |
| "METHOD_MEAN": 1, |
| "METHOD_MAX": 2, |
| "METHOD_MIN": 3, |
| "METHOD_SUM": 4, |
| "METHOD_LATEST": 5, |
| } |
| ) |
| |
| func (x PickTimeSeriesFilter_Method) Enum() *PickTimeSeriesFilter_Method { |
| p := new(PickTimeSeriesFilter_Method) |
| *p = x |
| return p |
| } |
| |
| func (x PickTimeSeriesFilter_Method) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (PickTimeSeriesFilter_Method) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_monitoring_dashboard_v1_common_proto_enumTypes[2].Descriptor() |
| } |
| |
| func (PickTimeSeriesFilter_Method) Type() protoreflect.EnumType { |
| return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[2] |
| } |
| |
| func (x PickTimeSeriesFilter_Method) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use PickTimeSeriesFilter_Method.Descriptor instead. |
| func (PickTimeSeriesFilter_Method) EnumDescriptor() ([]byte, []int) { |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{1, 0} |
| } |
| |
| // Describes the ranking directions. |
| type PickTimeSeriesFilter_Direction int32 |
| |
| const ( |
| // Not allowed in well-formed requests. |
| PickTimeSeriesFilter_DIRECTION_UNSPECIFIED PickTimeSeriesFilter_Direction = 0 |
| // Pass the highest ranking inputs. |
| PickTimeSeriesFilter_TOP PickTimeSeriesFilter_Direction = 1 |
| // Pass the lowest ranking inputs. |
| PickTimeSeriesFilter_BOTTOM PickTimeSeriesFilter_Direction = 2 |
| ) |
| |
| // Enum value maps for PickTimeSeriesFilter_Direction. |
| var ( |
| PickTimeSeriesFilter_Direction_name = map[int32]string{ |
| 0: "DIRECTION_UNSPECIFIED", |
| 1: "TOP", |
| 2: "BOTTOM", |
| } |
| PickTimeSeriesFilter_Direction_value = map[string]int32{ |
| "DIRECTION_UNSPECIFIED": 0, |
| "TOP": 1, |
| "BOTTOM": 2, |
| } |
| ) |
| |
| func (x PickTimeSeriesFilter_Direction) Enum() *PickTimeSeriesFilter_Direction { |
| p := new(PickTimeSeriesFilter_Direction) |
| *p = x |
| return p |
| } |
| |
| func (x PickTimeSeriesFilter_Direction) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (PickTimeSeriesFilter_Direction) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_monitoring_dashboard_v1_common_proto_enumTypes[3].Descriptor() |
| } |
| |
| func (PickTimeSeriesFilter_Direction) Type() protoreflect.EnumType { |
| return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[3] |
| } |
| |
| func (x PickTimeSeriesFilter_Direction) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use PickTimeSeriesFilter_Direction.Descriptor instead. |
| func (PickTimeSeriesFilter_Direction) EnumDescriptor() ([]byte, []int) { |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{1, 1} |
| } |
| |
| // The filter methods that can be applied to a stream. |
| type StatisticalTimeSeriesFilter_Method int32 |
| |
| const ( |
| // Not allowed in well-formed requests. |
| StatisticalTimeSeriesFilter_METHOD_UNSPECIFIED StatisticalTimeSeriesFilter_Method = 0 |
| // Compute the outlier score of each stream. |
| StatisticalTimeSeriesFilter_METHOD_CLUSTER_OUTLIER StatisticalTimeSeriesFilter_Method = 1 |
| ) |
| |
| // Enum value maps for StatisticalTimeSeriesFilter_Method. |
| var ( |
| StatisticalTimeSeriesFilter_Method_name = map[int32]string{ |
| 0: "METHOD_UNSPECIFIED", |
| 1: "METHOD_CLUSTER_OUTLIER", |
| } |
| StatisticalTimeSeriesFilter_Method_value = map[string]int32{ |
| "METHOD_UNSPECIFIED": 0, |
| "METHOD_CLUSTER_OUTLIER": 1, |
| } |
| ) |
| |
| func (x StatisticalTimeSeriesFilter_Method) Enum() *StatisticalTimeSeriesFilter_Method { |
| p := new(StatisticalTimeSeriesFilter_Method) |
| *p = x |
| return p |
| } |
| |
| func (x StatisticalTimeSeriesFilter_Method) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (StatisticalTimeSeriesFilter_Method) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_monitoring_dashboard_v1_common_proto_enumTypes[4].Descriptor() |
| } |
| |
| func (StatisticalTimeSeriesFilter_Method) Type() protoreflect.EnumType { |
| return &file_google_monitoring_dashboard_v1_common_proto_enumTypes[4] |
| } |
| |
| func (x StatisticalTimeSeriesFilter_Method) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use StatisticalTimeSeriesFilter_Method.Descriptor instead. |
| func (StatisticalTimeSeriesFilter_Method) EnumDescriptor() ([]byte, []int) { |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{2, 0} |
| } |
| |
| // Describes how to combine multiple time series to provide different views of |
| // the data. Aggregation consists of an alignment step on individual time |
| // series (`alignment_period` and `per_series_aligner`) followed by an optional |
| // reduction step of the data across the aligned time series |
| // (`cross_series_reducer` and `group_by_fields`). For more details, see |
| // [Aggregation](https://cloud.google.com/monitoring/api/learn_more#aggregation). |
| type Aggregation struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The alignment period for per-[time series][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. |
| AlignmentPeriod *duration.Duration `protobuf:"bytes,1,opt,name=alignment_period,json=alignmentPeriod,proto3" json:"alignment_period,omitempty"` |
| // 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. |
| 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"` |
| // 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. |
| 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"` |
| // 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. |
| GroupByFields []string `protobuf:"bytes,5,rep,name=group_by_fields,json=groupByFields,proto3" json:"group_by_fields,omitempty"` |
| } |
| |
| func (x *Aggregation) Reset() { |
| *x = Aggregation{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[0] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *Aggregation) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*Aggregation) ProtoMessage() {} |
| |
| func (x *Aggregation) ProtoReflect() protoreflect.Message { |
| mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[0] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use Aggregation.ProtoReflect.Descriptor instead. |
| func (*Aggregation) Descriptor() ([]byte, []int) { |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{0} |
| } |
| |
| func (x *Aggregation) GetAlignmentPeriod() *duration.Duration { |
| if x != nil { |
| return x.AlignmentPeriod |
| } |
| return nil |
| } |
| |
| func (x *Aggregation) GetPerSeriesAligner() Aggregation_Aligner { |
| if x != nil { |
| return x.PerSeriesAligner |
| } |
| return Aggregation_ALIGN_NONE |
| } |
| |
| func (x *Aggregation) GetCrossSeriesReducer() Aggregation_Reducer { |
| if x != nil { |
| return x.CrossSeriesReducer |
| } |
| return Aggregation_REDUCE_NONE |
| } |
| |
| func (x *Aggregation) GetGroupByFields() []string { |
| if x != nil { |
| return x.GroupByFields |
| } |
| return nil |
| } |
| |
| // Describes a ranking-based time series filter. Each input time series is |
| // ranked with an aligner. The filter lets through up to `num_time_series` time |
| // series, selecting them based on the relative ranking. |
| type PickTimeSeriesFilter struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // `rankingMethod` is applied to each time series independently to produce the |
| // value which will be used to compare the time series to other time series. |
| RankingMethod PickTimeSeriesFilter_Method `protobuf:"varint,1,opt,name=ranking_method,json=rankingMethod,proto3,enum=google.monitoring.dashboard.v1.PickTimeSeriesFilter_Method" json:"ranking_method,omitempty"` |
| // How many time series to return. |
| NumTimeSeries int32 `protobuf:"varint,2,opt,name=num_time_series,json=numTimeSeries,proto3" json:"num_time_series,omitempty"` |
| // How to use the ranking to select time series that pass through the filter. |
| Direction PickTimeSeriesFilter_Direction `protobuf:"varint,3,opt,name=direction,proto3,enum=google.monitoring.dashboard.v1.PickTimeSeriesFilter_Direction" json:"direction,omitempty"` |
| } |
| |
| func (x *PickTimeSeriesFilter) Reset() { |
| *x = PickTimeSeriesFilter{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[1] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *PickTimeSeriesFilter) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*PickTimeSeriesFilter) ProtoMessage() {} |
| |
| func (x *PickTimeSeriesFilter) ProtoReflect() protoreflect.Message { |
| mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[1] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use PickTimeSeriesFilter.ProtoReflect.Descriptor instead. |
| func (*PickTimeSeriesFilter) Descriptor() ([]byte, []int) { |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{1} |
| } |
| |
| func (x *PickTimeSeriesFilter) GetRankingMethod() PickTimeSeriesFilter_Method { |
| if x != nil { |
| return x.RankingMethod |
| } |
| return PickTimeSeriesFilter_METHOD_UNSPECIFIED |
| } |
| |
| func (x *PickTimeSeriesFilter) GetNumTimeSeries() int32 { |
| if x != nil { |
| return x.NumTimeSeries |
| } |
| return 0 |
| } |
| |
| func (x *PickTimeSeriesFilter) GetDirection() PickTimeSeriesFilter_Direction { |
| if x != nil { |
| return x.Direction |
| } |
| return PickTimeSeriesFilter_DIRECTION_UNSPECIFIED |
| } |
| |
| // A filter that ranks streams based on their statistical relation to other |
| // streams in a request. |
| type StatisticalTimeSeriesFilter struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // `rankingMethod` is applied to a set of time series, and then the produced |
| // value for each individual time series is used to compare a given time |
| // series to others. |
| // These are methods that cannot be applied stream-by-stream, but rather |
| // require the full context of a request to evaluate time series. |
| RankingMethod StatisticalTimeSeriesFilter_Method `protobuf:"varint,1,opt,name=ranking_method,json=rankingMethod,proto3,enum=google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter_Method" json:"ranking_method,omitempty"` |
| // How many time series to output. |
| NumTimeSeries int32 `protobuf:"varint,2,opt,name=num_time_series,json=numTimeSeries,proto3" json:"num_time_series,omitempty"` |
| } |
| |
| func (x *StatisticalTimeSeriesFilter) Reset() { |
| *x = StatisticalTimeSeriesFilter{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[2] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *StatisticalTimeSeriesFilter) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*StatisticalTimeSeriesFilter) ProtoMessage() {} |
| |
| func (x *StatisticalTimeSeriesFilter) ProtoReflect() protoreflect.Message { |
| mi := &file_google_monitoring_dashboard_v1_common_proto_msgTypes[2] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use StatisticalTimeSeriesFilter.ProtoReflect.Descriptor instead. |
| func (*StatisticalTimeSeriesFilter) Descriptor() ([]byte, []int) { |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP(), []int{2} |
| } |
| |
| func (x *StatisticalTimeSeriesFilter) GetRankingMethod() StatisticalTimeSeriesFilter_Method { |
| if x != nil { |
| return x.RankingMethod |
| } |
| return StatisticalTimeSeriesFilter_METHOD_UNSPECIFIED |
| } |
| |
| func (x *StatisticalTimeSeriesFilter) GetNumTimeSeries() int32 { |
| if x != nil { |
| return x.NumTimeSeries |
| } |
| return 0 |
| } |
| |
| var File_google_monitoring_dashboard_v1_common_proto protoreflect.FileDescriptor |
| |
| var file_google_monitoring_dashboard_v1_common_proto_rawDesc = []byte{ |
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| } |
| |
| var ( |
| file_google_monitoring_dashboard_v1_common_proto_rawDescOnce sync.Once |
| file_google_monitoring_dashboard_v1_common_proto_rawDescData = file_google_monitoring_dashboard_v1_common_proto_rawDesc |
| ) |
| |
| func file_google_monitoring_dashboard_v1_common_proto_rawDescGZIP() []byte { |
| file_google_monitoring_dashboard_v1_common_proto_rawDescOnce.Do(func() { |
| file_google_monitoring_dashboard_v1_common_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_monitoring_dashboard_v1_common_proto_rawDescData) |
| }) |
| return file_google_monitoring_dashboard_v1_common_proto_rawDescData |
| } |
| |
| var file_google_monitoring_dashboard_v1_common_proto_enumTypes = make([]protoimpl.EnumInfo, 5) |
| var file_google_monitoring_dashboard_v1_common_proto_msgTypes = make([]protoimpl.MessageInfo, 3) |
| var file_google_monitoring_dashboard_v1_common_proto_goTypes = []interface{}{ |
| (Aggregation_Aligner)(0), // 0: google.monitoring.dashboard.v1.Aggregation.Aligner |
| (Aggregation_Reducer)(0), // 1: google.monitoring.dashboard.v1.Aggregation.Reducer |
| (PickTimeSeriesFilter_Method)(0), // 2: google.monitoring.dashboard.v1.PickTimeSeriesFilter.Method |
| (PickTimeSeriesFilter_Direction)(0), // 3: google.monitoring.dashboard.v1.PickTimeSeriesFilter.Direction |
| (StatisticalTimeSeriesFilter_Method)(0), // 4: google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter.Method |
| (*Aggregation)(nil), // 5: google.monitoring.dashboard.v1.Aggregation |
| (*PickTimeSeriesFilter)(nil), // 6: google.monitoring.dashboard.v1.PickTimeSeriesFilter |
| (*StatisticalTimeSeriesFilter)(nil), // 7: google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter |
| (*duration.Duration)(nil), // 8: google.protobuf.Duration |
| } |
| var file_google_monitoring_dashboard_v1_common_proto_depIdxs = []int32{ |
| 8, // 0: google.monitoring.dashboard.v1.Aggregation.alignment_period:type_name -> google.protobuf.Duration |
| 0, // 1: google.monitoring.dashboard.v1.Aggregation.per_series_aligner:type_name -> google.monitoring.dashboard.v1.Aggregation.Aligner |
| 1, // 2: google.monitoring.dashboard.v1.Aggregation.cross_series_reducer:type_name -> google.monitoring.dashboard.v1.Aggregation.Reducer |
| 2, // 3: google.monitoring.dashboard.v1.PickTimeSeriesFilter.ranking_method:type_name -> google.monitoring.dashboard.v1.PickTimeSeriesFilter.Method |
| 3, // 4: google.monitoring.dashboard.v1.PickTimeSeriesFilter.direction:type_name -> google.monitoring.dashboard.v1.PickTimeSeriesFilter.Direction |
| 4, // 5: google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter.ranking_method:type_name -> google.monitoring.dashboard.v1.StatisticalTimeSeriesFilter.Method |
| 6, // [6:6] is the sub-list for method output_type |
| 6, // [6:6] is the sub-list for method input_type |
| 6, // [6:6] is the sub-list for extension type_name |
| 6, // [6:6] is the sub-list for extension extendee |
| 0, // [0:6] is the sub-list for field type_name |
| } |
| |
| func init() { file_google_monitoring_dashboard_v1_common_proto_init() } |
| func file_google_monitoring_dashboard_v1_common_proto_init() { |
| if File_google_monitoring_dashboard_v1_common_proto != nil { |
| return |
| } |
| if !protoimpl.UnsafeEnabled { |
| file_google_monitoring_dashboard_v1_common_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*Aggregation); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_monitoring_dashboard_v1_common_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*PickTimeSeriesFilter); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_monitoring_dashboard_v1_common_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*StatisticalTimeSeriesFilter); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| } |
| type x struct{} |
| out := protoimpl.TypeBuilder{ |
| File: protoimpl.DescBuilder{ |
| GoPackagePath: reflect.TypeOf(x{}).PkgPath(), |
| RawDescriptor: file_google_monitoring_dashboard_v1_common_proto_rawDesc, |
| NumEnums: 5, |
| NumMessages: 3, |
| NumExtensions: 0, |
| NumServices: 0, |
| }, |
| GoTypes: file_google_monitoring_dashboard_v1_common_proto_goTypes, |
| DependencyIndexes: file_google_monitoring_dashboard_v1_common_proto_depIdxs, |
| EnumInfos: file_google_monitoring_dashboard_v1_common_proto_enumTypes, |
| MessageInfos: file_google_monitoring_dashboard_v1_common_proto_msgTypes, |
| }.Build() |
| File_google_monitoring_dashboard_v1_common_proto = out.File |
| file_google_monitoring_dashboard_v1_common_proto_rawDesc = nil |
| file_google_monitoring_dashboard_v1_common_proto_goTypes = nil |
| file_google_monitoring_dashboard_v1_common_proto_depIdxs = nil |
| } |