// 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. If both
//   start and end times are specified, they must be identical. 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) }

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