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// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/monitoring/v3/common.proto
package monitoring
import (
fmt "fmt"
math "math"
proto "github.com/golang/protobuf/proto"
duration "github.com/golang/protobuf/ptypes/duration"
timestamp "github.com/golang/protobuf/ptypes/timestamp"
distribution "google.golang.org/genproto/googleapis/api/distribution"
)
// Reference imports to suppress errors if they are not otherwise used.
var _ = proto.Marshal
var _ = fmt.Errorf
var _ = math.Inf
// This is a compile-time assertion to ensure that this generated file
// is compatible with the proto package it is being compiled against.
// A compilation error at this line likely means your copy of the
// proto package needs to be updated.
const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package
// Specifies an ordering relationship on two arguments, here called left and
// right.
type ComparisonType int32
const (
// No ordering relationship is specified.
ComparisonType_COMPARISON_UNSPECIFIED ComparisonType = 0
// The left argument is greater than the right argument.
ComparisonType_COMPARISON_GT ComparisonType = 1
// The left argument is greater than or equal to the right argument.
ComparisonType_COMPARISON_GE ComparisonType = 2
// The left argument is less than the right argument.
ComparisonType_COMPARISON_LT ComparisonType = 3
// The left argument is less than or equal to the right argument.
ComparisonType_COMPARISON_LE ComparisonType = 4
// The left argument is equal to the right argument.
ComparisonType_COMPARISON_EQ ComparisonType = 5
// The left argument is not equal to the right argument.
ComparisonType_COMPARISON_NE ComparisonType = 6
)
var ComparisonType_name = map[int32]string{
0: "COMPARISON_UNSPECIFIED",
1: "COMPARISON_GT",
2: "COMPARISON_GE",
3: "COMPARISON_LT",
4: "COMPARISON_LE",
5: "COMPARISON_EQ",
6: "COMPARISON_NE",
}
var ComparisonType_value = map[string]int32{
"COMPARISON_UNSPECIFIED": 0,
"COMPARISON_GT": 1,
"COMPARISON_GE": 2,
"COMPARISON_LT": 3,
"COMPARISON_LE": 4,
"COMPARISON_EQ": 5,
"COMPARISON_NE": 6,
}
func (x ComparisonType) String() string {
return proto.EnumName(ComparisonType_name, int32(x))
}
func (ComparisonType) EnumDescriptor() ([]byte, []int) {
return fileDescriptor_013c57c1dcbb8d65, []int{0}
}
// The tier of service for a Workspace. Please see the
// [service tiers
// documentation](https://cloud.google.com/monitoring/workspaces/tiers) for more
// details.
type ServiceTier int32 // Deprecated: Do not use.
const (
// An invalid sentinel value, used to indicate that a tier has not
// been provided explicitly.
ServiceTier_SERVICE_TIER_UNSPECIFIED ServiceTier = 0
// The Stackdriver Basic tier, a free tier of service that provides basic
// features, a moderate allotment of logs, and access to built-in metrics.
// A number of features are not available in this tier. For more details,
// see [the service tiers
// documentation](https://cloud.google.com/monitoring/workspaces/tiers).
ServiceTier_SERVICE_TIER_BASIC ServiceTier = 1
// The Stackdriver Premium tier, a higher, more expensive tier of service
// that provides access to all Stackdriver features, lets you use Stackdriver
// with AWS accounts, and has a larger allotments for logs and metrics. For
// more details, see [the service tiers
// documentation](https://cloud.google.com/monitoring/workspaces/tiers).
ServiceTier_SERVICE_TIER_PREMIUM ServiceTier = 2
)
var ServiceTier_name = map[int32]string{
0: "SERVICE_TIER_UNSPECIFIED",
1: "SERVICE_TIER_BASIC",
2: "SERVICE_TIER_PREMIUM",
}
var ServiceTier_value = map[string]int32{
"SERVICE_TIER_UNSPECIFIED": 0,
"SERVICE_TIER_BASIC": 1,
"SERVICE_TIER_PREMIUM": 2,
}
func (x ServiceTier) String() string {
return proto.EnumName(ServiceTier_name, int32(x))
}
func (ServiceTier) EnumDescriptor() ([]byte, []int) {
return fileDescriptor_013c57c1dcbb8d65, []int{1}
}
// 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
)
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",
}
var 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) String() string {
return proto.EnumName(Aggregation_Aligner_name, int32(x))
}
func (Aggregation_Aligner) EnumDescriptor() ([]byte, []int) {
return fileDescriptor_013c57c1dcbb8d65, []int{2, 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
)
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",
}
var 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) String() string {
return proto.EnumName(Aggregation_Reducer_name, int32(x))
}
func (Aggregation_Reducer) EnumDescriptor() ([]byte, []int) {
return fileDescriptor_013c57c1dcbb8d65, []int{2, 1}
}
// A single strongly-typed value.
type TypedValue struct {
// The typed value field.
//
// Types that are valid to be assigned to Value:
// *TypedValue_BoolValue
// *TypedValue_Int64Value
// *TypedValue_DoubleValue
// *TypedValue_StringValue
// *TypedValue_DistributionValue
Value isTypedValue_Value `protobuf_oneof:"value"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *TypedValue) Reset() { *m = TypedValue{} }
func (m *TypedValue) String() string { return proto.CompactTextString(m) }
func (*TypedValue) ProtoMessage() {}
func (*TypedValue) Descriptor() ([]byte, []int) {
return fileDescriptor_013c57c1dcbb8d65, []int{0}
}
func (m *TypedValue) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_TypedValue.Unmarshal(m, b)
}
func (m *TypedValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_TypedValue.Marshal(b, m, deterministic)
}
func (m *TypedValue) XXX_Merge(src proto.Message) {
xxx_messageInfo_TypedValue.Merge(m, src)
}
func (m *TypedValue) XXX_Size() int {
return xxx_messageInfo_TypedValue.Size(m)
}
func (m *TypedValue) XXX_DiscardUnknown() {
xxx_messageInfo_TypedValue.DiscardUnknown(m)
}
var xxx_messageInfo_TypedValue proto.InternalMessageInfo
type isTypedValue_Value interface {
isTypedValue_Value()
}
type TypedValue_BoolValue struct {
BoolValue bool `protobuf:"varint,1,opt,name=bool_value,json=boolValue,proto3,oneof"`
}
type TypedValue_Int64Value struct {
Int64Value int64 `protobuf:"varint,2,opt,name=int64_value,json=int64Value,proto3,oneof"`
}
type TypedValue_DoubleValue struct {
DoubleValue float64 `protobuf:"fixed64,3,opt,name=double_value,json=doubleValue,proto3,oneof"`
}
type TypedValue_StringValue struct {
StringValue string `protobuf:"bytes,4,opt,name=string_value,json=stringValue,proto3,oneof"`
}
type TypedValue_DistributionValue struct {
DistributionValue *distribution.Distribution `protobuf:"bytes,5,opt,name=distribution_value,json=distributionValue,proto3,oneof"`
}
func (*TypedValue_BoolValue) isTypedValue_Value() {}
func (*TypedValue_Int64Value) isTypedValue_Value() {}
func (*TypedValue_DoubleValue) isTypedValue_Value() {}
func (*TypedValue_StringValue) isTypedValue_Value() {}
func (*TypedValue_DistributionValue) isTypedValue_Value() {}
func (m *TypedValue) GetValue() isTypedValue_Value {
if m != nil {
return m.Value
}
return nil
}
func (m *TypedValue) GetBoolValue() bool {
if x, ok := m.GetValue().(*TypedValue_BoolValue); ok {
return x.BoolValue
}
return false
}
func (m *TypedValue) GetInt64Value() int64 {
if x, ok := m.GetValue().(*TypedValue_Int64Value); ok {
return x.Int64Value
}
return 0
}
func (m *TypedValue) GetDoubleValue() float64 {
if x, ok := m.GetValue().(*TypedValue_DoubleValue); ok {
return x.DoubleValue
}
return 0
}
func (m *TypedValue) GetStringValue() string {
if x, ok := m.GetValue().(*TypedValue_StringValue); ok {
return x.StringValue
}
return ""
}
func (m *TypedValue) GetDistributionValue() *distribution.Distribution {
if x, ok := m.GetValue().(*TypedValue_DistributionValue); ok {
return x.DistributionValue
}
return nil
}
// XXX_OneofWrappers is for the internal use of the proto package.
func (*TypedValue) XXX_OneofWrappers() []interface{} {
return []interface{}{
(*TypedValue_BoolValue)(nil),
(*TypedValue_Int64Value)(nil),
(*TypedValue_DoubleValue)(nil),
(*TypedValue_StringValue)(nil),
(*TypedValue_DistributionValue)(nil),
}
}
// A closed time interval. It extends from the start time to the end time, and includes both: `[startTime, endTime]`. Valid time intervals depend on the [`MetricKind`](/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors#MetricKind) of the metric value. In no case can the end time be earlier than the start time.
//
// * For a `GAUGE` metric, the `startTime` value is technically optional; if
// no value is specified, the start time defaults to the value of the
// end time, and the interval represents a single point in time. Such an
// interval is valid only for `GAUGE` metrics, which are point-in-time
// measurements.
//
// * For `DELTA` and `CUMULATIVE` metrics, the start time must be earlier
// than the end time.
//
// * In all cases, the start time of the next interval must be
// at least a microsecond after the end time of the previous interval.
// Because the interval is closed, if the start time of a new interval
// is the same as the end time of the previous interval, data written
// at the new start time could overwrite data written at the previous
// end time.
type TimeInterval struct {
// Required. The end of the time interval.
EndTime *timestamp.Timestamp `protobuf:"bytes,2,opt,name=end_time,json=endTime,proto3" json:"end_time,omitempty"`
// Optional. The beginning of the time interval. The default value
// for the start time is the end time. The start time must not be
// later than the end time.
StartTime *timestamp.Timestamp `protobuf:"bytes,1,opt,name=start_time,json=startTime,proto3" json:"start_time,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *TimeInterval) Reset() { *m = TimeInterval{} }
func (m *TimeInterval) String() string { return proto.CompactTextString(m) }
func (*TimeInterval) ProtoMessage() {}
func (*TimeInterval) Descriptor() ([]byte, []int) {
return fileDescriptor_013c57c1dcbb8d65, []int{1}
}
func (m *TimeInterval) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_TimeInterval.Unmarshal(m, b)
}
func (m *TimeInterval) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_TimeInterval.Marshal(b, m, deterministic)
}
func (m *TimeInterval) XXX_Merge(src proto.Message) {
xxx_messageInfo_TimeInterval.Merge(m, src)
}
func (m *TimeInterval) XXX_Size() int {
return xxx_messageInfo_TimeInterval.Size(m)
}
func (m *TimeInterval) XXX_DiscardUnknown() {
xxx_messageInfo_TimeInterval.DiscardUnknown(m)
}
var xxx_messageInfo_TimeInterval proto.InternalMessageInfo
func (m *TimeInterval) GetEndTime() *timestamp.Timestamp {
if m != nil {
return m.EndTime
}
return nil
}
func (m *TimeInterval) GetStartTime() *timestamp.Timestamp {
if m != nil {
return m.StartTime
}
return nil
}
// 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](/monitoring/api/learn_more#aggregation).
type Aggregation struct {
// The alignment period for per-[time series][google.monitoring.v3.TimeSeries]
// alignment. If present, `alignmentPeriod` must be at least 60
// seconds. After per-time series alignment, each time series will
// contain data points only on the period boundaries. If
// `perSeriesAligner` is not specified or equals `ALIGN_NONE`, then
// this field is ignored. If `perSeriesAligner` is specified and
// does not equal `ALIGN_NONE`, then this field must be defined;
// otherwise an error is returned.
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.v3.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.v3.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"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *Aggregation) Reset() { *m = Aggregation{} }
func (m *Aggregation) String() string { return proto.CompactTextString(m) }
func (*Aggregation) ProtoMessage() {}
func (*Aggregation) Descriptor() ([]byte, []int) {
return fileDescriptor_013c57c1dcbb8d65, []int{2}
}
func (m *Aggregation) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_Aggregation.Unmarshal(m, b)
}
func (m *Aggregation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_Aggregation.Marshal(b, m, deterministic)
}
func (m *Aggregation) XXX_Merge(src proto.Message) {
xxx_messageInfo_Aggregation.Merge(m, src)
}
func (m *Aggregation) XXX_Size() int {
return xxx_messageInfo_Aggregation.Size(m)
}
func (m *Aggregation) XXX_DiscardUnknown() {
xxx_messageInfo_Aggregation.DiscardUnknown(m)
}
var xxx_messageInfo_Aggregation proto.InternalMessageInfo
func (m *Aggregation) GetAlignmentPeriod() *duration.Duration {
if m != nil {
return m.AlignmentPeriod
}
return nil
}
func (m *Aggregation) GetPerSeriesAligner() Aggregation_Aligner {
if m != nil {
return m.PerSeriesAligner
}
return Aggregation_ALIGN_NONE
}
func (m *Aggregation) GetCrossSeriesReducer() Aggregation_Reducer {
if m != nil {
return m.CrossSeriesReducer
}
return Aggregation_REDUCE_NONE
}
func (m *Aggregation) GetGroupByFields() []string {
if m != nil {
return m.GroupByFields
}
return nil
}
func init() {
proto.RegisterEnum("google.monitoring.v3.ComparisonType", ComparisonType_name, ComparisonType_value)
proto.RegisterEnum("google.monitoring.v3.ServiceTier", ServiceTier_name, ServiceTier_value)
proto.RegisterEnum("google.monitoring.v3.Aggregation_Aligner", Aggregation_Aligner_name, Aggregation_Aligner_value)
proto.RegisterEnum("google.monitoring.v3.Aggregation_Reducer", Aggregation_Reducer_name, Aggregation_Reducer_value)
proto.RegisterType((*TypedValue)(nil), "google.monitoring.v3.TypedValue")
proto.RegisterType((*TimeInterval)(nil), "google.monitoring.v3.TimeInterval")
proto.RegisterType((*Aggregation)(nil), "google.monitoring.v3.Aggregation")
}
func init() { proto.RegisterFile("google/monitoring/v3/common.proto", fileDescriptor_013c57c1dcbb8d65) }
var fileDescriptor_013c57c1dcbb8d65 = []byte{
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}
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