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common.pb.go
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common.pb.go
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// Code generated by protoc-gen-goten-go
// File: edgelq/monitoring/proto/v4/common.proto
// DO NOT EDIT!!!
package common
import (
"fmt"
"reflect"
"sync"
"google.golang.org/protobuf/encoding/protojson"
"google.golang.org/protobuf/proto"
preflect "google.golang.org/protobuf/reflect/protoreflect"
"google.golang.org/protobuf/runtime/protoimpl"
)
// proto imports
import (
durationpb "google.golang.org/protobuf/types/known/durationpb"
timestamppb "google.golang.org/protobuf/types/known/timestamppb"
)
// Reference imports to suppress errors if they are not otherwise used.
var (
_ = fmt.Errorf
_ = reflect.Method{}
_ = sync.Once{}
_ = protojson.MarshalOptions{}
_ = proto.MarshalOptions{}
_ = preflect.Value{}
_ = protoimpl.DescBuilder{}
)
// make sure we're using proto imports
var (
_ = &durationpb.Duration{}
_ = ×tamppb.Timestamp{}
)
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)
)
// Controls which fields are returned by `ListTimeSeries`.
type TimeSeriesView int32
const (
// Returns the identity of the metric(s), the time series,
// and the time series data.
TimeSeriesView_FULL TimeSeriesView = 0
// Returns the identity of the metric and the time series resource,
// but not the time series data.
TimeSeriesView_HEADERS TimeSeriesView = 1
)
var (
TimeSeriesView_name = map[int32]string{
0: "FULL",
1: "HEADERS",
}
TimeSeriesView_value = map[string]int32{
"FULL": 0,
"HEADERS": 1,
}
)
func (x TimeSeriesView) Enum() *TimeSeriesView {
p := new(TimeSeriesView)
*p = x
return p
}
func (x TimeSeriesView) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), preflect.EnumNumber(x))
}
func (TimeSeriesView) Descriptor() preflect.EnumDescriptor {
return edgelq_monitoring_proto_v4_common_proto_enumTypes[0].Descriptor()
}
func (TimeSeriesView) Type() preflect.EnumType {
return &edgelq_monitoring_proto_v4_common_proto_enumTypes[0]
}
func (x TimeSeriesView) Number() preflect.EnumNumber {
return preflect.EnumNumber(x)
}
// Deprecated, Use TimeSeriesView.ProtoReflect.Descriptor instead.
func (TimeSeriesView) EnumDescriptor() ([]byte, []int) {
return edgelq_monitoring_proto_v4_common_proto_rawDescGZIP(), []int{0}
}
// Value types that can be used as label values.
type LabelDescriptor_ValueType int32
const (
// A variable-length string. This is the default.
LabelDescriptor_STRING LabelDescriptor_ValueType = 0
// Boolean; true or false.
LabelDescriptor_BOOL LabelDescriptor_ValueType = 1
// A 64-bit signed integer.
LabelDescriptor_INT64 LabelDescriptor_ValueType = 2
)
var (
LabelDescriptor_ValueType_name = map[int32]string{
0: "STRING",
1: "BOOL",
2: "INT64",
}
LabelDescriptor_ValueType_value = map[string]int32{
"STRING": 0,
"BOOL": 1,
"INT64": 2,
}
)
func (x LabelDescriptor_ValueType) Enum() *LabelDescriptor_ValueType {
p := new(LabelDescriptor_ValueType)
*p = x
return p
}
func (x LabelDescriptor_ValueType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), preflect.EnumNumber(x))
}
func (LabelDescriptor_ValueType) Descriptor() preflect.EnumDescriptor {
return edgelq_monitoring_proto_v4_common_proto_enumTypes[1].Descriptor()
}
func (LabelDescriptor_ValueType) Type() preflect.EnumType {
return &edgelq_monitoring_proto_v4_common_proto_enumTypes[1]
}
func (x LabelDescriptor_ValueType) Number() preflect.EnumNumber {
return preflect.EnumNumber(x)
}
// Deprecated, Use LabelDescriptor_ValueType.ProtoReflect.Descriptor instead.
func (LabelDescriptor_ValueType) EnumDescriptor() ([]byte, []int) {
return edgelq_monitoring_proto_v4_common_proto_rawDescGZIP(), []int{0, 0}
}
// 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
// Outputs Distribution without bucketing with stats like: Min, Max, Count,
// Mean, SumOfSquaredDeviations valid only for LONG, DOUBLE and DISTRIBUTION
// value types
Aggregation_ALIGN_SUMMARY Aggregation_Aligner = 45
// TDigest Summary allows for calculation (and further aggregation) of
// percentiles
Aggregation_ALIGN_TDIGEST_SUMMARY Aggregation_Aligner = 46
)
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",
45: "ALIGN_SUMMARY",
46: "ALIGN_TDIGEST_SUMMARY",
}
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,
"ALIGN_SUMMARY": 45,
"ALIGN_TDIGEST_SUMMARY": 46,
}
)
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(), preflect.EnumNumber(x))
}
func (Aggregation_Aligner) Descriptor() preflect.EnumDescriptor {
return edgelq_monitoring_proto_v4_common_proto_enumTypes[2].Descriptor()
}
func (Aggregation_Aligner) Type() preflect.EnumType {
return &edgelq_monitoring_proto_v4_common_proto_enumTypes[2]
}
func (x Aggregation_Aligner) Number() preflect.EnumNumber {
return preflect.EnumNumber(x)
}
// Deprecated, Use Aggregation_Aligner.ProtoReflect.Descriptor instead.
func (Aggregation_Aligner) EnumDescriptor() ([]byte, []int) {
return edgelq_monitoring_proto_v4_common_proto_rawDescGZIP(), []int{6, 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
// Reduce with Distribution with stats like: Min, Max, Count, Mean,
// SumOfSquaredDeviations, histogram. This reducer is valid for gauge and
// delta metrics of numeric and distribution type. The value of the output
// is [DISTRIBUTION][google.api.MetricDescriptor.ValueType.DISTRIBUTION]
Aggregation_REDUCE_SUMMARY Aggregation_Reducer = 13
)
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",
13: "REDUCE_SUMMARY",
}
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,
"REDUCE_SUMMARY": 13,
}
)
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(), preflect.EnumNumber(x))
}
func (Aggregation_Reducer) Descriptor() preflect.EnumDescriptor {
return edgelq_monitoring_proto_v4_common_proto_enumTypes[3].Descriptor()
}
func (Aggregation_Reducer) Type() preflect.EnumType {
return &edgelq_monitoring_proto_v4_common_proto_enumTypes[3]
}
func (x Aggregation_Reducer) Number() preflect.EnumNumber {
return preflect.EnumNumber(x)
}
// Deprecated, Use Aggregation_Reducer.ProtoReflect.Descriptor instead.
func (Aggregation_Reducer) EnumDescriptor() ([]byte, []int) {
return edgelq_monitoring_proto_v4_common_proto_rawDescGZIP(), []int{6, 1}
}
// A description of a label.
type LabelDescriptor struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The label key.
Key string `protobuf:"bytes,1,opt,name=key,proto3" json:"key,omitempty" firestore:"key"`
// The type of data that can be assigned to the label.
ValueType LabelDescriptor_ValueType `protobuf:"varint,2,opt,name=value_type,json=valueType,proto3,enum=ntt.monitoring.v4.LabelDescriptor_ValueType" json:"value_type,omitempty" firestore:"valueType"`
// A human-readable description for the label.
Description string `protobuf:"bytes,3,opt,name=description,proto3" json:"description,omitempty" firestore:"description"`
// Default value for string label - this value is used in two cases:
// 1. to populate missing labels while creating TimeSeries
// 2. to populate missing remaining kvs while querying TimeSeries - usually
// applies to old data
DefaultValue string `protobuf:"bytes,12,opt,name=default_value,json=defaultValue,proto3" json:"default_value,omitempty" firestore:"defaultValue"`
// disabled flag communicates that this label is ignored by the backend. It's
// used for backward compatibility.
Disabled bool `protobuf:"varint,13,opt,name=disabled,proto3" json:"disabled,omitempty" firestore:"disabled"`
}
func (m *LabelDescriptor) Reset() {
*m = LabelDescriptor{}
if protoimpl.UnsafeEnabled {
mi := &edgelq_monitoring_proto_v4_common_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(m))
ms.StoreMessageInfo(mi)
}
}
func (m *LabelDescriptor) String() string {
return protoimpl.X.MessageStringOf(m)
}
func (*LabelDescriptor) ProtoMessage() {}
func (m *LabelDescriptor) ProtoReflect() preflect.Message {
mi := &edgelq_monitoring_proto_v4_common_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && m != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(m))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(m)
}
func (*LabelDescriptor) GotenMessage() {}
// Deprecated, Use LabelDescriptor.ProtoReflect.Descriptor instead.
func (*LabelDescriptor) Descriptor() ([]byte, []int) {
return edgelq_monitoring_proto_v4_common_proto_rawDescGZIP(), []int{0}
}
func (m *LabelDescriptor) Unmarshal(b []byte) error {
return proto.Unmarshal(b, m)
}
func (m *LabelDescriptor) Marshal() ([]byte, error) {
return proto.Marshal(m)
}
func (m *LabelDescriptor) MarshalJSON() ([]byte, error) {
return protojson.MarshalOptions{}.Marshal(m)
}
func (m *LabelDescriptor) UnmarshalJSON(data []byte) error {
return protojson.Unmarshal(data, m)
}
func (m *LabelDescriptor) GetKey() string {
if m != nil {
return m.Key
}
return ""
}
func (m *LabelDescriptor) GetValueType() LabelDescriptor_ValueType {
if m != nil {
return m.ValueType
}
return LabelDescriptor_STRING
}
func (m *LabelDescriptor) GetDescription() string {
if m != nil {
return m.Description
}
return ""
}
func (m *LabelDescriptor) GetDefaultValue() string {
if m != nil {
return m.DefaultValue
}
return ""
}
func (m *LabelDescriptor) GetDisabled() bool {
if m != nil {
return m.Disabled
}
return false
}
func (m *LabelDescriptor) SetKey(fv string) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "Key", "LabelDescriptor"))
}
m.Key = fv
}
func (m *LabelDescriptor) SetValueType(fv LabelDescriptor_ValueType) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "ValueType", "LabelDescriptor"))
}
m.ValueType = fv
}
func (m *LabelDescriptor) SetDescription(fv string) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "Description", "LabelDescriptor"))
}
m.Description = fv
}
func (m *LabelDescriptor) SetDefaultValue(fv string) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "DefaultValue", "LabelDescriptor"))
}
m.DefaultValue = fv
}
func (m *LabelDescriptor) SetDisabled(fv bool) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "Disabled", "LabelDescriptor"))
}
m.Disabled = fv
}
// LabelKeySet is used for defining PromotedLabelKeySets on Metric descriptors
type LabelKeySet struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
LabelKeys []string `protobuf:"bytes,1,rep,name=label_keys,json=labelKeys,proto3" json:"label_keys,omitempty" firestore:"labelKeys"`
// if set, index will not be considered for queries, but will be written to.
// useful for transition periods.
WriteOnly bool `protobuf:"varint,2,opt,name=write_only,json=writeOnly,proto3" json:"write_only,omitempty" firestore:"writeOnly"`
}
func (m *LabelKeySet) Reset() {
*m = LabelKeySet{}
if protoimpl.UnsafeEnabled {
mi := &edgelq_monitoring_proto_v4_common_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(m))
ms.StoreMessageInfo(mi)
}
}
func (m *LabelKeySet) String() string {
return protoimpl.X.MessageStringOf(m)
}
func (*LabelKeySet) ProtoMessage() {}
func (m *LabelKeySet) ProtoReflect() preflect.Message {
mi := &edgelq_monitoring_proto_v4_common_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && m != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(m))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(m)
}
func (*LabelKeySet) GotenMessage() {}
// Deprecated, Use LabelKeySet.ProtoReflect.Descriptor instead.
func (*LabelKeySet) Descriptor() ([]byte, []int) {
return edgelq_monitoring_proto_v4_common_proto_rawDescGZIP(), []int{1}
}
func (m *LabelKeySet) Unmarshal(b []byte) error {
return proto.Unmarshal(b, m)
}
func (m *LabelKeySet) Marshal() ([]byte, error) {
return proto.Marshal(m)
}
func (m *LabelKeySet) MarshalJSON() ([]byte, error) {
return protojson.MarshalOptions{}.Marshal(m)
}
func (m *LabelKeySet) UnmarshalJSON(data []byte) error {
return protojson.Unmarshal(data, m)
}
func (m *LabelKeySet) GetLabelKeys() []string {
if m != nil {
return m.LabelKeys
}
return nil
}
func (m *LabelKeySet) GetWriteOnly() bool {
if m != nil {
return m.WriteOnly
}
return false
}
func (m *LabelKeySet) SetLabelKeys(fv []string) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "LabelKeys", "LabelKeySet"))
}
m.LabelKeys = fv
}
func (m *LabelKeySet) SetWriteOnly(fv bool) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "WriteOnly", "LabelKeySet"))
}
m.WriteOnly = fv
}
// Distribution contains summary statistics for a population of values and,
// optionally, a histogram representing the distribution of those values across
// a specified set of histogram buckets.
//
// The summary statistics are the count, mean, sum of the squared deviation from
// the mean, the minimum, and the maximum of the set of population of values.
//
// The histogram is based on a sequence of buckets and gives a count of values
// that fall into each bucket. The boundaries of the buckets are given either
// explicitly or by specifying parameters for a method of computing them
// (buckets of fixed width or buckets of exponentially increasing width).
//
// Although it is not forbidden, it is generally a bad idea to include
// non-finite values (infinities or NaNs) in the population of values, as this
// will render the `mean` and `sum_of_squared_deviation` fields meaningless.
type Distribution struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The number of values in the population. Must be non-negative.
Count int64 `protobuf:"varint,1,opt,name=count,proto3" json:"count,omitempty" firestore:"count"`
// The arithmetic mean of the values in the population. If `count` is zero
// then this field must be zero.
Mean float64 `protobuf:"fixed64,2,opt,name=mean,proto3" json:"mean,omitempty" firestore:"mean"`
// The sum of squared deviations from the mean of the values in the
// population. For values x_i this is:
//
// Sum[i=1..n]((x_i - mean)^2)
//
// Knuth, "The Art of Computer Programming", Vol. 2, page 323, 3rd edition
// describes Welford's method for accumulating this sum in one pass.
//
// If `count` is zero then this field must be zero.
SumOfSquaredDeviation float64 `protobuf:"fixed64,3,opt,name=sum_of_squared_deviation,json=sumOfSquaredDeviation,proto3" json:"sum_of_squared_deviation,omitempty" firestore:"sumOfSquaredDeviation"`
// If specified, contains the range of the population values. The field
// must not be present if the `count` is zero.
Range *Distribution_Range `protobuf:"bytes,4,opt,name=range,proto3" json:"range,omitempty" firestore:"range"`
// Defines the histogram bucket boundaries.
BucketOptions *Distribution_BucketOptions `protobuf:"bytes,6,opt,name=bucket_options,json=bucketOptions,proto3" json:"bucket_options,omitempty" firestore:"bucketOptions"`
// If `bucket_options` is given, then the sum of the values in `bucket_counts`
// must equal the value in `count`. If `bucket_options` is not given, no
// `bucket_counts` fields may be given.
//
// Bucket counts are given in order under the numbering scheme described
// above (the underflow bucket has number 0; the finite buckets, if any,
// have numbers 1 through N-2; the overflow bucket has number N-1).
//
// The size of `bucket_counts` must be no greater than N as defined in
// `bucket_options`.
//
// Any suffix of trailing zero bucket_count fields may be omitted.
BucketCounts []int64 `protobuf:"varint,7,rep,packed,name=bucket_counts,json=bucketCounts,proto3" json:"bucket_counts,omitempty" firestore:"bucketCounts"`
}
func (m *Distribution) Reset() {
*m = Distribution{}
if protoimpl.UnsafeEnabled {
mi := &edgelq_monitoring_proto_v4_common_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(m))
ms.StoreMessageInfo(mi)
}
}
func (m *Distribution) String() string {
return protoimpl.X.MessageStringOf(m)
}
func (*Distribution) ProtoMessage() {}
func (m *Distribution) ProtoReflect() preflect.Message {
mi := &edgelq_monitoring_proto_v4_common_proto_msgTypes[2]
if protoimpl.UnsafeEnabled && m != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(m))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(m)
}
func (*Distribution) GotenMessage() {}
// Deprecated, Use Distribution.ProtoReflect.Descriptor instead.
func (*Distribution) Descriptor() ([]byte, []int) {
return edgelq_monitoring_proto_v4_common_proto_rawDescGZIP(), []int{2}
}
func (m *Distribution) Unmarshal(b []byte) error {
return proto.Unmarshal(b, m)
}
func (m *Distribution) Marshal() ([]byte, error) {
return proto.Marshal(m)
}
func (m *Distribution) MarshalJSON() ([]byte, error) {
return protojson.MarshalOptions{}.Marshal(m)
}
func (m *Distribution) UnmarshalJSON(data []byte) error {
return protojson.Unmarshal(data, m)
}
func (m *Distribution) GetCount() int64 {
if m != nil {
return m.Count
}
return int64(0)
}
func (m *Distribution) GetMean() float64 {
if m != nil {
return m.Mean
}
return float64(0)
}
func (m *Distribution) GetSumOfSquaredDeviation() float64 {
if m != nil {
return m.SumOfSquaredDeviation
}
return float64(0)
}
func (m *Distribution) GetRange() *Distribution_Range {
if m != nil {
return m.Range
}
return nil
}
func (m *Distribution) GetBucketOptions() *Distribution_BucketOptions {
if m != nil {
return m.BucketOptions
}
return nil
}
func (m *Distribution) GetBucketCounts() []int64 {
if m != nil {
return m.BucketCounts
}
return nil
}
func (m *Distribution) SetCount(fv int64) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "Count", "Distribution"))
}
m.Count = fv
}
func (m *Distribution) SetMean(fv float64) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "Mean", "Distribution"))
}
m.Mean = fv
}
func (m *Distribution) SetSumOfSquaredDeviation(fv float64) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "SumOfSquaredDeviation", "Distribution"))
}
m.SumOfSquaredDeviation = fv
}
func (m *Distribution) SetRange(fv *Distribution_Range) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "Range", "Distribution"))
}
m.Range = fv
}
func (m *Distribution) SetBucketOptions(fv *Distribution_BucketOptions) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "BucketOptions", "Distribution"))
}
m.BucketOptions = fv
}
func (m *Distribution) SetBucketCounts(fv []int64) {
if m == nil {
panic(fmt.Errorf("can't set %s on nil %s", "BucketCounts", "Distribution"))
}
m.BucketCounts = fv
}
// A single strongly-typed value.
type TypedValue struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// 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{}
if protoimpl.UnsafeEnabled {
mi := &edgelq_monitoring_proto_v4_common_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(m))
ms.StoreMessageInfo(mi)
}
}
func (m *TypedValue) String() string {
return protoimpl.X.MessageStringOf(m)
}
func (*TypedValue) ProtoMessage() {}
func (m *TypedValue) ProtoReflect() preflect.Message {
mi := &edgelq_monitoring_proto_v4_common_proto_msgTypes[3]
if protoimpl.UnsafeEnabled && m != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(m))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(m)
}
func (*TypedValue) GotenMessage() {}
// Deprecated, Use TypedValue.ProtoReflect.Descriptor instead.
func (*TypedValue) Descriptor() ([]byte, []int) {
return edgelq_monitoring_proto_v4_common_proto_rawDescGZIP(), []int{3}
}
func (m *TypedValue) Unmarshal(b []byte) error {
return proto.Unmarshal(b, m)
}
func (m *TypedValue) Marshal() ([]byte, error) {
return proto.Marshal(m)
}
func (m *TypedValue) MarshalJSON() ([]byte, error) {
return protojson.MarshalOptions{}.Marshal(m)
}