// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/monitoring/v3/common.proto

/*
Package monitoring is a generated protocol buffer package.

It is generated from these files:
	google/monitoring/v3/common.proto
	google/monitoring/v3/group.proto
	google/monitoring/v3/group_service.proto
	google/monitoring/v3/metric.proto
	google/monitoring/v3/metric_service.proto

It has these top-level messages:
	TypedValue
	TimeInterval
	Aggregation
	Group
	ListGroupsRequest
	ListGroupsResponse
	GetGroupRequest
	CreateGroupRequest
	UpdateGroupRequest
	DeleteGroupRequest
	ListGroupMembersRequest
	ListGroupMembersResponse
	Point
	TimeSeries
	ListMonitoredResourceDescriptorsRequest
	ListMonitoredResourceDescriptorsResponse
	GetMonitoredResourceDescriptorRequest
	ListMetricDescriptorsRequest
	ListMetricDescriptorsResponse
	GetMetricDescriptorRequest
	CreateMetricDescriptorRequest
	DeleteMetricDescriptorRequest
	ListTimeSeriesRequest
	ListTimeSeriesResponse
	CreateTimeSeriesRequest
	CreateTimeSeriesError
*/
package monitoring

import proto "github.com/golang/protobuf/proto"
import fmt "fmt"
import math "math"
import google_api2 "google.golang.org/genproto/googleapis/api/distribution"
import google_protobuf3 "github.com/golang/protobuf/ptypes/duration"
import google_protobuf2 "github.com/golang/protobuf/ptypes/timestamp"

// 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.ProtoPackageIsVersion2 // please upgrade the proto package

// 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.
	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].
	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 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
)

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",
	17: "ALIGN_FRACTION_TRUE",
	18: "ALIGN_PERCENTILE_99",
	19: "ALIGN_PERCENTILE_95",
	20: "ALIGN_PERCENTILE_50",
	21: "ALIGN_PERCENTILE_05",
}
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_FRACTION_TRUE": 17,
	"ALIGN_PERCENTILE_99": 18,
	"ALIGN_PERCENTILE_95": 19,
	"ALIGN_PERCENTILE_50": 20,
	"ALIGN_PERCENTILE_05": 21,
}

func (x Aggregation_Aligner) String() string {
	return proto.EnumName(Aggregation_Aligner_name, int32(x))
}
func (Aggregation_Aligner) EnumDescriptor() ([]byte, []int) { return fileDescriptor0, []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 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",
	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_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 fileDescriptor0, []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"`
}

func (m *TypedValue) Reset()                    { *m = TypedValue{} }
func (m *TypedValue) String() string            { return proto.CompactTextString(m) }
func (*TypedValue) ProtoMessage()               {}
func (*TypedValue) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{0} }

type isTypedValue_Value interface {
	isTypedValue_Value()
}

type TypedValue_BoolValue struct {
	BoolValue bool `protobuf:"varint,1,opt,name=bool_value,json=boolValue,oneof"`
}
type TypedValue_Int64Value struct {
	Int64Value int64 `protobuf:"varint,2,opt,name=int64_value,json=int64Value,oneof"`
}
type TypedValue_DoubleValue struct {
	DoubleValue float64 `protobuf:"fixed64,3,opt,name=double_value,json=doubleValue,oneof"`
}
type TypedValue_StringValue struct {
	StringValue string `protobuf:"bytes,4,opt,name=string_value,json=stringValue,oneof"`
}
type TypedValue_DistributionValue struct {
	DistributionValue *google_api2.Distribution `protobuf:"bytes,5,opt,name=distribution_value,json=distributionValue,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() *google_api2.Distribution {
	if x, ok := m.GetValue().(*TypedValue_DistributionValue); ok {
		return x.DistributionValue
	}
	return nil
}

// XXX_OneofFuncs is for the internal use of the proto package.
func (*TypedValue) XXX_OneofFuncs() (func(msg proto.Message, b *proto.Buffer) error, func(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error), func(msg proto.Message) (n int), []interface{}) {
	return _TypedValue_OneofMarshaler, _TypedValue_OneofUnmarshaler, _TypedValue_OneofSizer, []interface{}{
		(*TypedValue_BoolValue)(nil),
		(*TypedValue_Int64Value)(nil),
		(*TypedValue_DoubleValue)(nil),
		(*TypedValue_StringValue)(nil),
		(*TypedValue_DistributionValue)(nil),
	}
}

func _TypedValue_OneofMarshaler(msg proto.Message, b *proto.Buffer) error {
	m := msg.(*TypedValue)
	// value
	switch x := m.Value.(type) {
	case *TypedValue_BoolValue:
		t := uint64(0)
		if x.BoolValue {
			t = 1
		}
		b.EncodeVarint(1<<3 | proto.WireVarint)
		b.EncodeVarint(t)
	case *TypedValue_Int64Value:
		b.EncodeVarint(2<<3 | proto.WireVarint)
		b.EncodeVarint(uint64(x.Int64Value))
	case *TypedValue_DoubleValue:
		b.EncodeVarint(3<<3 | proto.WireFixed64)
		b.EncodeFixed64(math.Float64bits(x.DoubleValue))
	case *TypedValue_StringValue:
		b.EncodeVarint(4<<3 | proto.WireBytes)
		b.EncodeStringBytes(x.StringValue)
	case *TypedValue_DistributionValue:
		b.EncodeVarint(5<<3 | proto.WireBytes)
		if err := b.EncodeMessage(x.DistributionValue); err != nil {
			return err
		}
	case nil:
	default:
		return fmt.Errorf("TypedValue.Value has unexpected type %T", x)
	}
	return nil
}

func _TypedValue_OneofUnmarshaler(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error) {
	m := msg.(*TypedValue)
	switch tag {
	case 1: // value.bool_value
		if wire != proto.WireVarint {
			return true, proto.ErrInternalBadWireType
		}
		x, err := b.DecodeVarint()
		m.Value = &TypedValue_BoolValue{x != 0}
		return true, err
	case 2: // value.int64_value
		if wire != proto.WireVarint {
			return true, proto.ErrInternalBadWireType
		}
		x, err := b.DecodeVarint()
		m.Value = &TypedValue_Int64Value{int64(x)}
		return true, err
	case 3: // value.double_value
		if wire != proto.WireFixed64 {
			return true, proto.ErrInternalBadWireType
		}
		x, err := b.DecodeFixed64()
		m.Value = &TypedValue_DoubleValue{math.Float64frombits(x)}
		return true, err
	case 4: // value.string_value
		if wire != proto.WireBytes {
			return true, proto.ErrInternalBadWireType
		}
		x, err := b.DecodeStringBytes()
		m.Value = &TypedValue_StringValue{x}
		return true, err
	case 5: // value.distribution_value
		if wire != proto.WireBytes {
			return true, proto.ErrInternalBadWireType
		}
		msg := new(google_api2.Distribution)
		err := b.DecodeMessage(msg)
		m.Value = &TypedValue_DistributionValue{msg}
		return true, err
	default:
		return false, nil
	}
}

func _TypedValue_OneofSizer(msg proto.Message) (n int) {
	m := msg.(*TypedValue)
	// value
	switch x := m.Value.(type) {
	case *TypedValue_BoolValue:
		n += proto.SizeVarint(1<<3 | proto.WireVarint)
		n += 1
	case *TypedValue_Int64Value:
		n += proto.SizeVarint(2<<3 | proto.WireVarint)
		n += proto.SizeVarint(uint64(x.Int64Value))
	case *TypedValue_DoubleValue:
		n += proto.SizeVarint(3<<3 | proto.WireFixed64)
		n += 8
	case *TypedValue_StringValue:
		n += proto.SizeVarint(4<<3 | proto.WireBytes)
		n += proto.SizeVarint(uint64(len(x.StringValue)))
		n += len(x.StringValue)
	case *TypedValue_DistributionValue:
		s := proto.Size(x.DistributionValue)
		n += proto.SizeVarint(5<<3 | proto.WireBytes)
		n += proto.SizeVarint(uint64(s))
		n += s
	case nil:
	default:
		panic(fmt.Sprintf("proto: unexpected type %T in oneof", x))
	}
	return n
}

// A time interval extending just after a start time through an end time.
// If the start time is the same as the end time, then the interval
// represents a single point in time.
type TimeInterval struct {
	// Required. The end of the time interval.
	EndTime *google_protobuf2.Timestamp `protobuf:"bytes,2,opt,name=end_time,json=endTime" 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 *google_protobuf2.Timestamp `protobuf:"bytes,1,opt,name=start_time,json=startTime" json:"start_time,omitempty"`
}

func (m *TimeInterval) Reset()                    { *m = TimeInterval{} }
func (m *TimeInterval) String() string            { return proto.CompactTextString(m) }
func (*TimeInterval) ProtoMessage()               {}
func (*TimeInterval) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{1} }

func (m *TimeInterval) GetEndTime() *google_protobuf2.Timestamp {
	if m != nil {
		return m.EndTime
	}
	return nil
}

func (m *TimeInterval) GetStartTime() *google_protobuf2.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 (`per_series_aligner`) followed by an optional reduction of the data
// across different time series (`cross_series_reducer`).  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 *google_protobuf3.Duration `protobuf:"bytes,1,opt,name=alignment_period,json=alignmentPeriod" 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,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,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" json:"group_by_fields,omitempty"`
}

func (m *Aggregation) Reset()                    { *m = Aggregation{} }
func (m *Aggregation) String() string            { return proto.CompactTextString(m) }
func (*Aggregation) ProtoMessage()               {}
func (*Aggregation) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{2} }

func (m *Aggregation) GetAlignmentPeriod() *google_protobuf3.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.RegisterType((*TypedValue)(nil), "google.monitoring.v3.TypedValue")
	proto.RegisterType((*TimeInterval)(nil), "google.monitoring.v3.TimeInterval")
	proto.RegisterType((*Aggregation)(nil), "google.monitoring.v3.Aggregation")
	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)
}

func init() { proto.RegisterFile("google/monitoring/v3/common.proto", fileDescriptor0) }

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