"""Generated message classes for monitoring version v3. Manages your Cloud Monitoring data and configurations. """ # NOTE: This file is autogenerated and should not be edited by hand. from __future__ import absolute_import from apitools.base.protorpclite import messages as _messages from apitools.base.py import encoding from apitools.base.py import extra_types package = 'monitoring' class Aggregation(_messages.Message): r"""Describes how to combine multiple time series to provide a different view of the data. Aggregation of time series is done in two steps. First, each time series in the set is aligned to the same time interval boundaries, then the set of time series is optionally reduced in number.Alignment consists of applying the per_series_aligner operation to each time series after its data has been divided into regular alignment_period time intervals. This process takes all of the data points in an alignment period, applies a mathematical transformation such as averaging, minimum, maximum, delta, etc., and converts them into a single data point per period.Reduction is when the aligned and transformed time series can optionally be combined, reducing the number of time series through similar mathematical transformations. Reduction involves applying a cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, and applying the reducer to each subset.The raw time series data can contain a huge amount of information from multiple sources. Alignment and reduction transforms this mass of data into a more manageable and representative collection of data, for example "the 95% latency across the average of all tasks in a cluster". This representative data can be more easily graphed and comprehended, and the individual time series data is still available for later drilldown. For more details, see Filtering and aggregation (https://cloud.google.com/monitoring/api/v3/aggregation). Enums: CrossSeriesReducerValueValuesEnum: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. PerSeriesAlignerValueValuesEnum: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Fields: alignmentPeriod: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies. crossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. groupByFields: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored. perSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross- time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. """ class CrossSeriesReducerValueValuesEnum(_messages.Enum): r"""The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Values: REDUCE_NONE: No cross-time series reduction. The output of the Aligner is returned. REDUCE_MEAN: Reduce by computing the mean value 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. REDUCE_MIN: Reduce by computing the minimum value 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. REDUCE_MAX: Reduce by computing the maximum value 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. REDUCE_SUM: 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. REDUCE_STDDEV: 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. REDUCE_COUNT: Reduce by computing the number 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. REDUCE_COUNT_TRUE: Reduce by computing the number 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. REDUCE_COUNT_FALSE: Reduce by computing the number 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. REDUCE_FRACTION_TRUE: Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_95: Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_50: Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_05: Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/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. """ 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 = 8 REDUCE_FRACTION_TRUE = 9 REDUCE_PERCENTILE_99 = 10 REDUCE_PERCENTILE_95 = 11 REDUCE_PERCENTILE_50 = 12 REDUCE_PERCENTILE_05 = 13 class PerSeriesAlignerValueValuesEnum(_messages.Enum): r"""An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Values: ALIGN_NONE: No alignment. Raw data is returned. Not valid if cross- series reduction is requested. The value_type of the result is the same as the value_type of the input. ALIGN_DELTA: Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input. ALIGN_RATE: Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead. ALIGN_INTERPOLATE: Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_NEXT_OLDER: Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MIN: Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MAX: Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MEAN: Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE. ALIGN_COUNT: Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64. ALIGN_SUM: Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_STDDEV: Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE. ALIGN_COUNT_TRUE: Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_COUNT_FALSE: Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_FRACTION_TRUE: Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. ALIGN_PERCENTILE_99: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_95: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_50: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_05: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENT_CHANGE: Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. 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. """ ALIGN_NONE = 0 ALIGN_DELTA = 1 ALIGN_RATE = 2 ALIGN_INTERPOLATE = 3 ALIGN_NEXT_OLDER = 4 ALIGN_MIN = 5 ALIGN_MAX = 6 ALIGN_MEAN = 7 ALIGN_COUNT = 8 ALIGN_SUM = 9 ALIGN_STDDEV = 10 ALIGN_COUNT_TRUE = 11 ALIGN_COUNT_FALSE = 12 ALIGN_FRACTION_TRUE = 13 ALIGN_PERCENTILE_99 = 14 ALIGN_PERCENTILE_95 = 15 ALIGN_PERCENTILE_50 = 16 ALIGN_PERCENTILE_05 = 17 ALIGN_PERCENT_CHANGE = 18 alignmentPeriod = _messages.StringField(1) crossSeriesReducer = _messages.EnumField('CrossSeriesReducerValueValuesEnum', 2) groupByFields = _messages.StringField(3, repeated=True) perSeriesAligner = _messages.EnumField('PerSeriesAlignerValueValuesEnum', 4) class Alert(_messages.Message): r"""An alert is the representation of a violation of an alert policy. It is a read-only resource that cannot be modified by the accompanied API. Enums: StateValueValuesEnum: Output only. The current state of the alert. Fields: closeTime: The time when the alert was closed. log: The log information associated with the alert. This field is only populated for log-based alerts. metadata: The metadata of the monitored resource. metric: The metric type and any metric labels preserved from the incident's generating condition. name: Identifier. The name of the alert.The format is: projects/[PROJECT_ID_OR_NUMBER]/alerts/[ALERT_ID] The [ALERT_ID] is a system-assigned unique identifier for the alert. openTime: The time when the alert was opened. policy: The snapshot of the alert policy that generated this alert. resource: The monitored resource type and any monitored resource labels preserved from the incident's generating condition. state: Output only. The current state of the alert. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The current state of the alert. Values: STATE_UNSPECIFIED: The alert state is unspecified. OPEN: The alert is open. CLOSED: The alert is closed. """ STATE_UNSPECIFIED = 0 OPEN = 1 CLOSED = 2 closeTime = _messages.StringField(1) log = _messages.MessageField('LogMetadata', 2) metadata = _messages.MessageField('MonitoredResourceMetadata', 3) metric = _messages.MessageField('Metric', 4) name = _messages.StringField(5) openTime = _messages.StringField(6) policy = _messages.MessageField('PolicySnapshot', 7) resource = _messages.MessageField('MonitoredResource', 8) state = _messages.EnumField('StateValueValuesEnum', 9) class AlertPolicy(_messages.Message): r"""A description of the conditions under which some aspect of your system is considered to be "unhealthy" and the ways to notify people or services about this state. For an overview of alerting policies, see Introduction to Alerting (https://cloud.google.com/monitoring/alerts/). Enums: CombinerValueValuesEnum: How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED. SeverityValueValuesEnum: Optional. The severity of an alerting policy indicates how important incidents generated by that policy are. The severity level will be displayed on the Incident detail page and in notifications. Messages: UserLabelsValue: User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric- names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values. Fields: alertStrategy: Control over how this alerting policy's notification channels are notified. combiner: How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED. conditions: A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. If condition_time_series_query_language is present, it must be the only condition. If condition_monitoring_query_language is present, it must be the only condition. creationRecord: A read-only record of the creation of the alerting policy. If provided in a call to create or update, this field will be ignored. displayName: A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.The convention for the display_name of a PrometheusQueryLanguageCondition is "{rule group name}/{alert name}", where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the display_name is not a unique key of the AlertPolicy. documentation: Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation. enabled: Whether or not the policy is enabled. On write, the default interpretation if unset is that the policy is enabled. On read, clients should not make any assumption about the state if it has not been populated. The field should always be populated on List and Get operations, unless a field projection has been specified that strips it out. mutationRecord: A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored. name: Identifier. Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request. notificationChannels: Identifies the notification channels to which notifications should be sent when incidents are opened or closed or when new violations occur on an already opened incident. Each element of this array corresponds to the name field in each of the NotificationChannel objects that are returned from the ListNotificationChannels method. The format of the entries in this field is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID] severity: Optional. The severity of an alerting policy indicates how important incidents generated by that policy are. The severity level will be displayed on the Incident detail page and in notifications. userLabels: User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric- names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values. validity: Read-only description of how the alerting policy is invalid. This field is only set when the alerting policy is invalid. An invalid alerting policy will not generate incidents. """ class CombinerValueValuesEnum(_messages.Enum): r"""How to combine the results of multiple conditions to determine if an incident should be opened. If condition_time_series_query_language is present, this must be COMBINE_UNSPECIFIED. Values: COMBINE_UNSPECIFIED: An unspecified combiner. AND: Combine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources. OR: Combine conditions using the logical OR operator. An incident is created if any of the listed conditions is met. AND_WITH_MATCHING_RESOURCE: Combine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource. """ COMBINE_UNSPECIFIED = 0 AND = 1 OR = 2 AND_WITH_MATCHING_RESOURCE = 3 class SeverityValueValuesEnum(_messages.Enum): r"""Optional. The severity of an alerting policy indicates how important incidents generated by that policy are. The severity level will be displayed on the Incident detail page and in notifications. Values: SEVERITY_UNSPECIFIED: No severity is specified. This is the default value. CRITICAL: This is the highest severity level. Use this if the problem could cause significant damage or downtime. ERROR: This is the medium severity level. Use this if the problem could cause minor damage or downtime. WARNING: This is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future. """ SEVERITY_UNSPECIFIED = 0 CRITICAL = 1 ERROR = 2 WARNING = 3 @encoding.MapUnrecognizedFields('additionalProperties') class UserLabelsValue(_messages.Message): r"""User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.Note that Prometheus {alert name} is a valid Prometheus label names (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels), whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values. Messages: AdditionalProperty: An additional property for a UserLabelsValue object. Fields: additionalProperties: Additional properties of type UserLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a UserLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) alertStrategy = _messages.MessageField('AlertStrategy', 1) combiner = _messages.EnumField('CombinerValueValuesEnum', 2) conditions = _messages.MessageField('Condition', 3, repeated=True) creationRecord = _messages.MessageField('MutationRecord', 4) displayName = _messages.StringField(5) documentation = _messages.MessageField('Documentation', 6) enabled = _messages.BooleanField(7) mutationRecord = _messages.MessageField('MutationRecord', 8) name = _messages.StringField(9) notificationChannels = _messages.StringField(10, repeated=True) severity = _messages.EnumField('SeverityValueValuesEnum', 11) userLabels = _messages.MessageField('UserLabelsValue', 12) validity = _messages.MessageField('Status', 13) class AlertStrategy(_messages.Message): r"""Control over how the notification channels in notification_channels are notified when this alert fires. Enums: NotificationPromptsValueListEntryValuesEnum: Fields: autoClose: If an alerting policy that was active has no data for this long, any open incidents will close notificationChannelStrategy: Control how notifications will be sent out, on a per-channel basis. notificationPrompts: For log-based alert policies, the notification prompts is always OPENED. For non log-based alert policies, the notification prompts can be OPENED or OPENED, CLOSED. notificationRateLimit: Required for log-based alerting policies, i.e. policies with a LogMatch condition.This limit is not implemented for alerting policies that do not have a LogMatch condition. """ class NotificationPromptsValueListEntryValuesEnum(_messages.Enum): r"""NotificationPromptsValueListEntryValuesEnum enum type. Values: NOTIFICATION_PROMPT_UNSPECIFIED: No strategy specified. Treated as error. OPENED: Notify when an incident is opened. CLOSED: Notify when an incident is closed. """ NOTIFICATION_PROMPT_UNSPECIFIED = 0 OPENED = 1 CLOSED = 2 autoClose = _messages.StringField(1) notificationChannelStrategy = _messages.MessageField('NotificationChannelStrategy', 2, repeated=True) notificationPrompts = _messages.EnumField('NotificationPromptsValueListEntryValuesEnum', 3, repeated=True) notificationRateLimit = _messages.MessageField('NotificationRateLimit', 4) class AppEngine(_messages.Message): r"""App Engine service. Learn more at https://cloud.google.com/appengine. Fields: moduleId: The ID of the App Engine module underlying this service. Corresponds to the module_id resource label in the gae_app monitored resource (https://cloud.google.com/monitoring/api/resources#tag_gae_app). """ moduleId = _messages.StringField(1) class AvailabilityCriteria(_messages.Message): r"""Future parameters for the availability SLI.""" class BasicAuthentication(_messages.Message): r"""The authentication parameters to provide to the specified resource or URL that requires a username and password. Currently, only Basic HTTP authentication (https://tools.ietf.org/html/rfc7617) is supported in Uptime checks. Fields: password: The password to use when authenticating with the HTTP server. username: The username to use when authenticating with the HTTP server. """ password = _messages.StringField(1) username = _messages.StringField(2) class BasicService(_messages.Message): r"""A well-known service type, defined by its service type and service labels. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api- structures#basic-svc-w-basic-sli). Messages: ServiceLabelsValue: Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo- monitoring/api/api-structures#basic-svc-w-basic-sli). Fields: serviceLabels: Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo- monitoring/api/api-structures#basic-svc-w-basic-sli). serviceType: The type of service that this basic service defines, e.g. APP_ENGINE service type. Documentation and valid values here (https://cloud.google.com/stackdriver/docs/solutions/slo- monitoring/api/api-structures#basic-svc-w-basic-sli). """ @encoding.MapUnrecognizedFields('additionalProperties') class ServiceLabelsValue(_messages.Message): r"""Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo- monitoring/api/api-structures#basic-svc-w-basic-sli). Messages: AdditionalProperty: An additional property for a ServiceLabelsValue object. Fields: additionalProperties: Additional properties of type ServiceLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ServiceLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) serviceLabels = _messages.MessageField('ServiceLabelsValue', 1) serviceType = _messages.StringField(2) class BasicSli(_messages.Message): r"""An SLI measuring performance on a well-known service type. Performance will be computed on the basis of pre-defined metrics. The type of the service_resource determines the metrics to use and the service_resource.labels and metric_labels are used to construct a monitoring filter to filter that metric down to just the data relevant to this service. Fields: availability: Good service is defined to be the count of requests made to this service that return successfully. latency: Good service is defined to be the count of requests made to this service that are fast enough with respect to latency.threshold. location: OPTIONAL: The set of locations to which this SLI is relevant. Telemetry from other locations will not be used to calculate performance for this SLI. If omitted, this SLI applies to all locations in which the Service has activity. For service types that don't support breaking down by location, setting this field will result in an error. method: OPTIONAL: The set of RPCs to which this SLI is relevant. Telemetry from other methods will not be used to calculate performance for this SLI. If omitted, this SLI applies to all the Service's methods. For service types that don't support breaking down by method, setting this field will result in an error. version: OPTIONAL: The set of API versions to which this SLI is relevant. Telemetry from other API versions will not be used to calculate performance for this SLI. If omitted, this SLI applies to all API versions. For service types that don't support breaking down by version, setting this field will result in an error. """ availability = _messages.MessageField('AvailabilityCriteria', 1) latency = _messages.MessageField('LatencyCriteria', 2) location = _messages.StringField(3, repeated=True) method = _messages.StringField(4, repeated=True) version = _messages.StringField(5, repeated=True) class BooleanTest(_messages.Message): r"""A test that uses an alerting result in a boolean column produced by the SQL query. Fields: column: Required. The name of the column containing the boolean value. If the value in a row is NULL, that row is ignored. """ column = _messages.StringField(1) class BucketOptions(_messages.Message): r"""BucketOptions describes the bucket boundaries used to create a histogram for the distribution. The buckets can be in a linear sequence, an exponential sequence, or each bucket can be specified explicitly. BucketOptions does not include the number of values in each bucket.A bucket has an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket must be strictly greater than the lower bound. The sequence of N buckets for a distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. Fields: explicitBuckets: The explicit buckets. exponentialBuckets: The exponential buckets. linearBuckets: The linear bucket. """ explicitBuckets = _messages.MessageField('Explicit', 1) exponentialBuckets = _messages.MessageField('Exponential', 2) linearBuckets = _messages.MessageField('Linear', 3) class CloudEndpoints(_messages.Message): r"""Cloud Endpoints service. Learn more at https://cloud.google.com/endpoints. Fields: service: The name of the Cloud Endpoints service underlying this service. Corresponds to the service resource label in the api monitored resource (https://cloud.google.com/monitoring/api/resources#tag_api). """ service = _messages.StringField(1) class CloudFunctionV2Target(_messages.Message): r"""A Synthetic Monitor deployed to a Cloud Functions V2 instance. Fields: cloudRunRevision: Output only. The cloud_run_revision Monitored Resource associated with the GCFv2. The Synthetic Monitor execution results (metrics, logs, and spans) are reported against this Monitored Resource. This field is output only. name: Required. Fully qualified GCFv2 resource name i.e. projects/{project}/locations/{location}/functions/{function} Required. """ cloudRunRevision = _messages.MessageField('MonitoredResource', 1) name = _messages.StringField(2) class CloudRun(_messages.Message): r"""Cloud Run service. Learn more at https://cloud.google.com/run. Fields: location: The location the service is run. Corresponds to the location resource label in the cloud_run_revision monitored resource (https://clo ud.google.com/monitoring/api/resources#tag_cloud_run_revision). serviceName: The name of the Cloud Run service. Corresponds to the service_name resource label in the cloud_run_revision monitored resource (https://cloud.google.com/monitoring/api/resources#tag_cloud_run_revisio n). """ location = _messages.StringField(1) serviceName = _messages.StringField(2) class ClusterIstio(_messages.Message): r"""Istio service scoped to a single Kubernetes cluster. Learn more at https://istio.io. Clusters running OSS Istio will have their services ingested as this type. Fields: clusterName: The name of the Kubernetes cluster in which this Istio service is defined. Corresponds to the cluster_name resource label in k8s_cluster resources. location: The location of the Kubernetes cluster in which this Istio service is defined. Corresponds to the location resource label in k8s_cluster resources. serviceName: The name of the Istio service underlying this service. Corresponds to the destination_service_name metric label in Istio metrics. serviceNamespace: The namespace of the Istio service underlying this service. Corresponds to the destination_service_namespace metric label in Istio metrics. """ clusterName = _messages.StringField(1) location = _messages.StringField(2) serviceName = _messages.StringField(3) serviceNamespace = _messages.StringField(4) class CollectdPayload(_messages.Message): r"""A collection of data points sent from a collectd-based plugin. See the collectd documentation for more information. Messages: MetadataValue: The measurement metadata. Example: "process_id" -> 12345 Fields: endTime: The end time of the interval. metadata: The measurement metadata. Example: "process_id" -> 12345 plugin: The name of the plugin. Example: "disk". pluginInstance: The instance name of the plugin Example: "hdcl". startTime: The start time of the interval. type: The measurement type. Example: "memory". typeInstance: The measurement type instance. Example: "used". values: The measured values during this time interval. Each value must have a different data_source_name. """ @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""The measurement metadata. Example: "process_id" -> 12345 Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Additional properties of type MetadataValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A TypedValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('TypedValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) endTime = _messages.StringField(1) metadata = _messages.MessageField('MetadataValue', 2) plugin = _messages.StringField(3) pluginInstance = _messages.StringField(4) startTime = _messages.StringField(5) type = _messages.StringField(6) typeInstance = _messages.StringField(7) values = _messages.MessageField('CollectdValue', 8, repeated=True) class CollectdPayloadError(_messages.Message): r"""Describes the error status for payloads that were not written. Fields: error: Records the error status for the payload. If this field is present, the partial errors for nested values won't be populated. index: The zero-based index in CreateCollectdTimeSeriesRequest.collectd_payloads. valueErrors: Records the error status for values that were not written due to an error.Failed payloads for which nothing is written will not include partial value errors. """ error = _messages.MessageField('Status', 1) index = _messages.IntegerField(2, variant=_messages.Variant.INT32) valueErrors = _messages.MessageField('CollectdValueError', 3, repeated=True) class CollectdValue(_messages.Message): r"""A single data point from a collectd-based plugin. Enums: DataSourceTypeValueValuesEnum: The type of measurement. Fields: dataSourceName: The data source for the collectd value. For example, there are two data sources for network measurements: "rx" and "tx". dataSourceType: The type of measurement. value: The measurement value. """ class DataSourceTypeValueValuesEnum(_messages.Enum): r"""The type of measurement. Values: UNSPECIFIED_DATA_SOURCE_TYPE: An unspecified data source type. This corresponds to google.api.MetricDescriptor.MetricKind.METRIC_KIND_UNSPECIFIED. GAUGE: An instantaneous measurement of a varying quantity. This corresponds to google.api.MetricDescriptor.MetricKind.GAUGE. COUNTER: A cumulative value over time. This corresponds to google.api.MetricDescriptor.MetricKind.CUMULATIVE. DERIVE: A rate of change of the measurement. ABSOLUTE: An amount of change since the last measurement interval. This corresponds to google.api.MetricDescriptor.MetricKind.DELTA. """ UNSPECIFIED_DATA_SOURCE_TYPE = 0 GAUGE = 1 COUNTER = 2 DERIVE = 3 ABSOLUTE = 4 dataSourceName = _messages.StringField(1) dataSourceType = _messages.EnumField('DataSourceTypeValueValuesEnum', 2) value = _messages.MessageField('TypedValue', 3) class CollectdValueError(_messages.Message): r"""Describes the error status for values that were not written. Fields: error: Records the error status for the value. index: The zero-based index in CollectdPayload.values within the parent CreateCollectdTimeSeriesRequest.collectd_payloads. """ error = _messages.MessageField('Status', 1) index = _messages.IntegerField(2, variant=_messages.Variant.INT32) class Condition(_messages.Message): r"""A condition is a true/false test that determines when an alerting policy should open an incident. If a condition evaluates to true, it signifies that something is wrong. Fields: conditionAbsent: A condition that checks that a time series continues to receive new data points. conditionMatchedLog: A condition that checks for log messages matching given constraints. If set, no other conditions can be present. conditionMonitoringQueryLanguage: A condition that uses the Monitoring Query Language to define alerts. conditionPrometheusQueryLanguage: A condition that uses the Prometheus query language to define alerts. conditionSql: A condition that periodically evaluates a SQL query result. conditionThreshold: A condition that compares a time series against a threshold. displayName: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy. name: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/ [POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted. """ conditionAbsent = _messages.MessageField('MetricAbsence', 1) conditionMatchedLog = _messages.MessageField('LogMatch', 2) conditionMonitoringQueryLanguage = _messages.MessageField('MonitoringQueryLanguageCondition', 3) conditionPrometheusQueryLanguage = _messages.MessageField('PrometheusQueryLanguageCondition', 4) conditionSql = _messages.MessageField('SqlCondition', 5) conditionThreshold = _messages.MessageField('MetricThreshold', 6) displayName = _messages.StringField(7) name = _messages.StringField(8) class ContentMatcher(_messages.Message): r"""Optional. Used to perform content matching. This allows matching based on substrings and regular expressions, together with their negations. Only the first 4 MB of an HTTP or HTTPS check's response (and the first 1 MB of a TCP check's response) are examined for purposes of content matching. Enums: MatcherValueValuesEnum: The type of content matcher that will be applied to the server output, compared to the content string when the check is run. Fields: content: String, regex or JSON content to match. Maximum 1024 bytes. An empty content string indicates no content matching is to be performed. jsonPathMatcher: Matcher information for MATCHES_JSON_PATH and NOT_MATCHES_JSON_PATH matcher: The type of content matcher that will be applied to the server output, compared to the content string when the check is run. """ class MatcherValueValuesEnum(_messages.Enum): r"""The type of content matcher that will be applied to the server output, compared to the content string when the check is run. Values: CONTENT_MATCHER_OPTION_UNSPECIFIED: No content matcher type specified (maintained for backward compatibility, but deprecated for future use). Treated as CONTAINS_STRING. CONTAINS_STRING: Selects substring matching. The match succeeds if the output contains the content string. This is the default value for checks without a matcher option, or where the value of matcher is CONTENT_MATCHER_OPTION_UNSPECIFIED. NOT_CONTAINS_STRING: Selects negation of substring matching. The match succeeds if the output does NOT contain the content string. MATCHES_REGEX: Selects regular-expression matching. The match succeeds if the output matches the regular expression specified in the content string. Regex matching is only supported for HTTP/HTTPS checks. NOT_MATCHES_REGEX: Selects negation of regular-expression matching. The match succeeds if the output does NOT match the regular expression specified in the content string. Regex matching is only supported for HTTP/HTTPS checks. MATCHES_JSON_PATH: Selects JSONPath matching. See JsonPathMatcher for details on when the match succeeds. JSONPath matching is only supported for HTTP/HTTPS checks. NOT_MATCHES_JSON_PATH: Selects JSONPath matching. See JsonPathMatcher for details on when the match succeeds. Succeeds when output does NOT match as specified. JSONPath is only supported for HTTP/HTTPS checks. """ CONTENT_MATCHER_OPTION_UNSPECIFIED = 0 CONTAINS_STRING = 1 NOT_CONTAINS_STRING = 2 MATCHES_REGEX = 3 NOT_MATCHES_REGEX = 4 MATCHES_JSON_PATH = 5 NOT_MATCHES_JSON_PATH = 6 content = _messages.StringField(1) jsonPathMatcher = _messages.MessageField('JsonPathMatcher', 2) matcher = _messages.EnumField('MatcherValueValuesEnum', 3) class CreateCollectdTimeSeriesRequest(_messages.Message): r"""The CreateCollectdTimeSeries request. Fields: collectdPayloads: The collectd payloads representing the time series data. You must not include more than a single point for each time series, so no two payloads can have the same values for all of the fields plugin, plugin_instance, type, and type_instance. collectdVersion: The version of collectd that collected the data. Example: "5.3.0-192.el6". resource: The monitored resource associated with the time series. """ collectdPayloads = _messages.MessageField('CollectdPayload', 1, repeated=True) collectdVersion = _messages.StringField(2) resource = _messages.MessageField('MonitoredResource', 3) class CreateCollectdTimeSeriesResponse(_messages.Message): r"""The CreateCollectdTimeSeries response. Fields: payloadErrors: Records the error status for points that were not written due to an error in the request.Failed requests for which nothing is written will return an error response instead. Requests where data points were rejected by the backend will set summary instead. summary: Aggregate statistics from writing the payloads. This field is omitted if all points were successfully written, so that the response is empty. This is for backwards compatibility with clients that log errors on any non-empty response. """ payloadErrors = _messages.MessageField('CollectdPayloadError', 1, repeated=True) summary = _messages.MessageField('CreateTimeSeriesSummary', 2) class CreateTimeSeriesRequest(_messages.Message): r"""The CreateTimeSeries request. Fields: timeSeries: Required. The new data to be added to a list of time series. Adds at most one data point to each of several time series. The new data point must be more recent than any other point in its time series. Each TimeSeries value must fully specify a unique time series by supplying all label values for the metric and the monitored resource.The maximum number of TimeSeries objects per Create request is 200. """ timeSeries = _messages.MessageField('TimeSeries', 1, repeated=True) class CreateTimeSeriesSummary(_messages.Message): r"""Summary of the result of a failed request to write data to a time series. Fields: errors: The number of points that failed to be written. Order is not guaranteed. successPointCount: The number of points that were successfully written. totalPointCount: The number of points in the request. """ errors = _messages.MessageField('Error', 1, repeated=True) successPointCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) totalPointCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class Criteria(_messages.Message): r"""Criteria specific to the AlertPolicys that this Snooze applies to. The Snooze will suppress alerts that come from one of the AlertPolicys whose names are supplied. Fields: filter: Optional. When you define a snooze, you can also define a filter for that snooze. The filter is a string containing one or more key-value pairs. The string uses the standard https://google.aip.dev/160 filter syntax. If you define a filter for a snooze, then the snooze can only apply to one alert policy. When the snooze is active, incidents won't be created when the incident would have key-value pairs (labels) that match those specified by the filter in the snooze.Snooze filters support resource, metric, and metadata labels. If multiple labels are used, then they must be connected with an AND operator. For example, the following filter applies the snooze to incidents that have a resource label with an instance ID of 1234567890, a metric label with an instance name of test_group, a metadata user label with a key of foo and a value of bar, and a metadata system label with a key of region and a value of us- central1: "filter": "resource.labels.instance_id=\"1234567890\" AND metric.labels.instance_name=\"test_group\" AND metadata.user_labels.foo=\"bar\" AND metadata.system_labels.region=\"us- central1\"" policies: The specific AlertPolicy names for the alert that should be snoozed. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID] There is a limit of 16 policies per snooze. This limit is checked during snooze creation. Exactly 1 alert policy is required if filter is specified at the same time. """ filter = _messages.StringField(1) policies = _messages.StringField(2, repeated=True) class Custom(_messages.Message): r"""Use a custom service to designate a service that you want to monitor when none of the other service types (like App Engine, Cloud Run, or a GKE type) matches your intended service. """ class Daily(_messages.Message): r"""Used to schedule the query to run every so many days. Fields: executionTime: Optional. The time of day (in UTC) at which the query should run. If left unspecified, the server picks an arbitrary time of day and runs the query at the same time each day. periodicity: Required. The number of days between runs. Must be greater than or equal to 1 day and less than or equal to 31 days. """ executionTime = _messages.MessageField('TimeOfDay', 1) periodicity = _messages.IntegerField(2, variant=_messages.Variant.INT32) class Distribution(_messages.Message): r"""Distribution contains summary statistics for a population of values. It optionally contains a histogram representing the distribution of those values across a set of 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 formulas for buckets of fixed or exponentially increasing widths.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. Fields: bucketCounts: Required in the Cloud Monitoring API v3. The values for each bucket specified in bucket_options. The sum of the values in bucketCounts must equal the value in the count field of the Distribution object. The order of the bucket counts follows the numbering schemes described for the three bucket types. The underflow bucket has number 0; the finite buckets, if any, have numbers 1 through N-2; and the overflow bucket has number N-1. The size of bucket_counts must not be greater than N. If the size is less than N, then the remaining buckets are assigned values of zero. bucketOptions: Required in the Cloud Monitoring API v3. Defines the histogram bucket boundaries. count: The number of values in the population. Must be non-negative. This value must equal the sum of the values in bucket_counts if a histogram is provided. exemplars: Must be in increasing order of value field. mean: The arithmetic mean of the values in the population. If count is zero then this field must be zero. range: If specified, contains the range of the population values. The field must not be present if the count is zero. This field is presently ignored by the Cloud Monitoring API v3. sumOfSquaredDeviation: 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 232, 3rd edition describes Welford's method for accumulating this sum in one pass.If count is zero then this field must be zero. """ bucketCounts = _messages.IntegerField(1, repeated=True) bucketOptions = _messages.MessageField('BucketOptions', 2) count = _messages.IntegerField(3) exemplars = _messages.MessageField('Exemplar', 4, repeated=True) mean = _messages.FloatField(5) range = _messages.MessageField('Range', 6) sumOfSquaredDeviation = _messages.FloatField(7) class DistributionCut(_messages.Message): r"""A DistributionCut defines a TimeSeries and thresholds used for measuring good service and total service. The TimeSeries must have ValueType = DISTRIBUTION and MetricKind = DELTA or MetricKind = CUMULATIVE. The computed good_service will be the estimated count of values in the Distribution that fall within the specified min and max. Fields: distributionFilter: A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) specifying a TimeSeries aggregating values. Must have ValueType = DISTRIBUTION and MetricKind = DELTA or MetricKind = CUMULATIVE. range: Range of values considered "good." For a one-sided range, set one bound to an infinite value. """ distributionFilter = _messages.StringField(1) range = _messages.MessageField('GoogleMonitoringV3Range', 2) class Documentation(_messages.Message): r"""Documentation that is included in the notifications and incidents pertaining to this policy. Fields: content: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables#doc-vars). links: Optional. Links to content such as playbooks, repositories, and other resources. This field can contain up to 3 entries. mimeType: The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information. subject: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject- length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables#doc-vars). If this field is missing or empty, a default subject line will be generated. """ content = _messages.StringField(1) links = _messages.MessageField('Link', 2, repeated=True) mimeType = _messages.StringField(3) subject = _messages.StringField(4) class DroppedLabels(_messages.Message): r"""A set of (label, value) pairs that were removed from a Distribution time series during aggregation and then added as an attachment to a Distribution.Exemplar.The full label set for the exemplars is constructed by using the dropped pairs in combination with the label values that remain on the aggregated Distribution time series. The constructed full label set can be used to identify the specific entity, such as the instance or job, which might be contributing to a long-tail. However, with dropped labels, the storage requirements are reduced because only the aggregated distribution values for a large group of time series are stored.Note that there are no guarantees on ordering of the labels from exemplar-to-exemplar and from distribution-to-distribution in the same stream, and there may be duplicates. It is up to clients to resolve any ambiguities. Messages: LabelValue: Map from label to its value, for all labels dropped in any aggregation. Fields: label: Map from label to its value, for all labels dropped in any aggregation. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelValue(_messages.Message): r"""Map from label to its value, for all labels dropped in any aggregation. Messages: AdditionalProperty: An additional property for a LabelValue object. Fields: additionalProperties: Additional properties of type LabelValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) label = _messages.MessageField('LabelValue', 1) class Empty(_messages.Message): r"""A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } """ class Error(_messages.Message): r"""Detailed information about an error category. Fields: pointCount: The number of points that couldn't be written because of status. status: The status of the requested write operation. """ pointCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) status = _messages.MessageField('Status', 2) class Exemplar(_messages.Message): r"""Exemplars are example points that may be used to annotate aggregated distribution values. They are metadata that gives information about a particular value added to a Distribution bucket, such as a trace ID that was active when a value was added. They may contain further information, such as a example values and timestamps, origin, etc. Messages: AttachmentsValueListEntry: A AttachmentsValueListEntry object. Fields: attachments: Contextual information about the example value. Examples are:Trace: type.googleapis.com/google.monitoring.v3.SpanContextLiteral string: type.googleapis.com/google.protobuf.StringValueLabels dropped during aggregation: type.googleapis.com/google.monitoring.v3.DroppedLabelsThere may be only a single attachment of any given message type in a single exemplar, and this is enforced by the system. timestamp: The observation (sampling) time of the above value. value: Value of the exemplar point. This value determines to which bucket the exemplar belongs. """ @encoding.MapUnrecognizedFields('additionalProperties') class AttachmentsValueListEntry(_messages.Message): r"""A AttachmentsValueListEntry object. Messages: AdditionalProperty: An additional property for a AttachmentsValueListEntry object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a AttachmentsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) attachments = _messages.MessageField('AttachmentsValueListEntry', 1, repeated=True) timestamp = _messages.StringField(2) value = _messages.FloatField(3) class Explicit(_messages.Message): r"""Specifies a set of buckets with arbitrary widths.There are size(bounds) + 1 (= N) buckets. Bucket i has the following boundaries:Upper bound (0 <= i < N-1): boundsi Lower bound (1 <= i < N); boundsi - 1The bounds field must contain at least one element. If bounds has only one element, then there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. Fields: bounds: The values must be monotonically increasing. """ bounds = _messages.FloatField(1, repeated=True) class Exponential(_messages.Message): r"""Specifies an exponential sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket.There are num_finite_buckets + 2 (= N) buckets. Bucket i has the following boundaries:Upper bound (0 <= i < N-1): scale * (growth_factor ^ i).Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). Fields: growthFactor: Must be greater than 1. numFiniteBuckets: Must be greater than 0. scale: Must be greater than 0. """ growthFactor = _messages.FloatField(1) numFiniteBuckets = _messages.IntegerField(2, variant=_messages.Variant.INT32) scale = _messages.FloatField(3) class Field(_messages.Message): r"""A single field of a message type.New usages of this message as an alternative to FieldDescriptorProto are strongly discouraged. This message does not reliability preserve all information necessary to model the schema and preserve semantics. Instead make use of FileDescriptorSet which preserves the necessary information. Enums: CardinalityValueValuesEnum: The field cardinality. KindValueValuesEnum: The field type. Fields: cardinality: The field cardinality. defaultValue: The string value of the default value of this field. Proto2 syntax only. jsonName: The field JSON name. kind: The field type. name: The field name. number: The field number. oneofIndex: The index of the field type in Type.oneofs, for message or enumeration types. The first type has index 1; zero means the type is not in the list. options: The protocol buffer options. packed: Whether to use alternative packed wire representation. typeUrl: The field type URL, without the scheme, for message or enumeration types. Example: "type.googleapis.com/google.protobuf.Timestamp". """ class CardinalityValueValuesEnum(_messages.Enum): r"""The field cardinality. Values: CARDINALITY_UNKNOWN: For fields with unknown cardinality. CARDINALITY_OPTIONAL: For optional fields. CARDINALITY_REQUIRED: For required fields. Proto2 syntax only. CARDINALITY_REPEATED: For repeated fields. """ CARDINALITY_UNKNOWN = 0 CARDINALITY_OPTIONAL = 1 CARDINALITY_REQUIRED = 2 CARDINALITY_REPEATED = 3 class KindValueValuesEnum(_messages.Enum): r"""The field type. Values: TYPE_UNKNOWN: Field type unknown. TYPE_DOUBLE: Field type double. TYPE_FLOAT: Field type float. TYPE_INT64: Field type int64. TYPE_UINT64: Field type uint64. TYPE_INT32: Field type int32. TYPE_FIXED64: Field type fixed64. TYPE_FIXED32: Field type fixed32. TYPE_BOOL: Field type bool. TYPE_STRING: Field type string. TYPE_GROUP: Field type group. Proto2 syntax only, and deprecated. TYPE_MESSAGE: Field type message. TYPE_BYTES: Field type bytes. TYPE_UINT32: Field type uint32. TYPE_ENUM: Field type enum. TYPE_SFIXED32: Field type sfixed32. TYPE_SFIXED64: Field type sfixed64. TYPE_SINT32: Field type sint32. TYPE_SINT64: Field type sint64. """ TYPE_UNKNOWN = 0 TYPE_DOUBLE = 1 TYPE_FLOAT = 2 TYPE_INT64 = 3 TYPE_UINT64 = 4 TYPE_INT32 = 5 TYPE_FIXED64 = 6 TYPE_FIXED32 = 7 TYPE_BOOL = 8 TYPE_STRING = 9 TYPE_GROUP = 10 TYPE_MESSAGE = 11 TYPE_BYTES = 12 TYPE_UINT32 = 13 TYPE_ENUM = 14 TYPE_SFIXED32 = 15 TYPE_SFIXED64 = 16 TYPE_SINT32 = 17 TYPE_SINT64 = 18 cardinality = _messages.EnumField('CardinalityValueValuesEnum', 1) defaultValue = _messages.StringField(2) jsonName = _messages.StringField(3) kind = _messages.EnumField('KindValueValuesEnum', 4) name = _messages.StringField(5) number = _messages.IntegerField(6, variant=_messages.Variant.INT32) oneofIndex = _messages.IntegerField(7, variant=_messages.Variant.INT32) options = _messages.MessageField('Option', 8, repeated=True) packed = _messages.BooleanField(9) typeUrl = _messages.StringField(10) class ForecastOptions(_messages.Message): r"""Options used when forecasting the time series and testing the predicted value against the threshold. Fields: forecastHorizon: Required. The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours. """ forecastHorizon = _messages.StringField(1) class GetNotificationChannelVerificationCodeRequest(_messages.Message): r"""The GetNotificationChannelVerificationCode request. Fields: expireTime: The desired expiration time. If specified, the API will guarantee that the returned code will not be valid after the specified timestamp; however, the API cannot guarantee that the returned code will be valid for at least as long as the requested time (the API puts an upper bound on the amount of time for which a code may be valid). If omitted, a default expiration will be used, which may be less than the max permissible expiration (so specifying an expiration may extend the code's lifetime over omitting an expiration, even though the API does impose an upper limit on the maximum expiration that is permitted). """ expireTime = _messages.StringField(1) class GetNotificationChannelVerificationCodeResponse(_messages.Message): r"""The GetNotificationChannelVerificationCode request. Fields: code: The verification code, which may be used to verify other channels that have an equivalent identity (i.e. other channels of the same type with the same fingerprint such as other email channels with the same email address or other sms channels with the same number). expireTime: The expiration time associated with the code that was returned. If an expiration was provided in the request, this is the minimum of the requested expiration in the request and the max permitted expiration. """ code = _messages.StringField(1) expireTime = _messages.StringField(2) class GkeNamespace(_messages.Message): r"""GKE Namespace. The field names correspond to the resource metadata labels on monitored resources that fall under a namespace (for example, k8s_container or k8s_pod). Fields: clusterName: The name of the parent cluster. location: The location of the parent cluster. This may be a zone or region. namespaceName: The name of this namespace. projectId: Output only. The project this resource lives in. For legacy services migrated from the Custom type, this may be a distinct project from the one parenting the service itself. """ clusterName = _messages.StringField(1) location = _messages.StringField(2) namespaceName = _messages.StringField(3) projectId = _messages.StringField(4) class GkeService(_messages.Message): r"""GKE Service. The "service" here represents a Kubernetes service object (https://kubernetes.io/docs/concepts/services-networking/service). The field names correspond to the resource labels on k8s_service monitored resources (https://cloud.google.com/monitoring/api/resources#tag_k8s_service). Fields: clusterName: The name of the parent cluster. location: The location of the parent cluster. This may be a zone or region. namespaceName: The name of the parent namespace. projectId: Output only. The project this resource lives in. For legacy services migrated from the Custom type, this may be a distinct project from the one parenting the service itself. serviceName: The name of this service. """ clusterName = _messages.StringField(1) location = _messages.StringField(2) namespaceName = _messages.StringField(3) projectId = _messages.StringField(4) serviceName = _messages.StringField(5) class GkeWorkload(_messages.Message): r"""A GKE Workload (Deployment, StatefulSet, etc). The field names correspond to the metadata labels on monitored resources that fall under a workload (for example, k8s_container or k8s_pod). Fields: clusterName: The name of the parent cluster. location: The location of the parent cluster. This may be a zone or region. namespaceName: The name of the parent namespace. projectId: Output only. The project this resource lives in. For legacy services migrated from the Custom type, this may be a distinct project from the one parenting the service itself. topLevelControllerName: The name of this workload. topLevelControllerType: The type of this workload (for example, "Deployment" or "DaemonSet") """ clusterName = _messages.StringField(1) location = _messages.StringField(2) namespaceName = _messages.StringField(3) projectId = _messages.StringField(4) topLevelControllerName = _messages.StringField(5) topLevelControllerType = _messages.StringField(6) class GoogleMonitoringV3Range(_messages.Message): r"""Range of numerical values within min and max. Fields: max: Range maximum. min: Range minimum. """ max = _messages.FloatField(1) min = _messages.FloatField(2) class Group(_messages.Message): r"""The description of a dynamic collection of monitored resources. Each group has a filter that is matched against monitored resources and their associated metadata. If a group's filter matches an available monitored resource, then that resource is a member of that group. Groups can contain any number of monitored resources, and each monitored resource can be a member of any number of groups.Groups can be nested in parent-child hierarchies. The parentName field identifies an optional parent for each group. If a group has a parent, then the only monitored resources available to be matched by the group's filter are the resources contained in the parent group. In other words, a group contains the monitored resources that match its filter and the filters of all the group's ancestors. A group without a parent can contain any monitored resource.For example, consider an infrastructure running a set of instances with two user-defined tags: "environment" and "role". A parent group has a filter, environment="production". A child of that parent group has a filter, role="transcoder". The parent group contains all instances in the production environment, regardless of their roles. The child group contains instances that have the transcoder role and are in the production environment.The monitored resources contained in a group can change at any moment, depending on what resources exist and what filters are associated with the group and its ancestors. Fields: displayName: A user-assigned name for this group, used only for display purposes. filter: The filter used to determine which monitored resources belong to this group. isCluster: If true, the members of this group are considered to be a cluster. The system can perform additional analysis on groups that are clusters. name: Output only. The name of this group. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] When creating a group, this field is ignored and a new name is created consisting of the project specified in the call to CreateGroup and a unique [GROUP_ID] that is generated automatically. parentName: The name of the group's parent, if it has one. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] For groups with no parent, parent_name is the empty string, "". """ displayName = _messages.StringField(1) filter = _messages.StringField(2) isCluster = _messages.BooleanField(3) name = _messages.StringField(4) parentName = _messages.StringField(5) class Hourly(_messages.Message): r"""Used to schedule the query to run every so many hours. Fields: minuteOffset: Optional. The number of minutes after the hour (in UTC) to run the query. Must be greater than or equal to 0 minutes and less than or equal to 59 minutes. If left unspecified, then an arbitrary offset is used. periodicity: Required. The number of hours between runs. Must be greater than or equal to 1 hour and less than or equal to 48 hours. """ minuteOffset = _messages.IntegerField(1, variant=_messages.Variant.INT32) periodicity = _messages.IntegerField(2, variant=_messages.Variant.INT32) class HttpCheck(_messages.Message): r"""Information involved in an HTTP/HTTPS Uptime check request. Enums: ContentTypeValueValuesEnum: The content type header to use for the check. The following configurations result in errors: 1. Content type is specified in both the headers field and the content_type field. 2. Request method is GET and content_type is not TYPE_UNSPECIFIED 3. Request method is POST and content_type is TYPE_UNSPECIFIED. 4. Request method is POST and a "Content-Type" header is provided via headers field. The content_type field should be used instead. RequestMethodValueValuesEnum: The HTTP request method to use for the check. If set to METHOD_UNSPECIFIED then request_method defaults to GET. Messages: HeadersValue: The list of headers to send as part of the Uptime check request. If two headers have the same key and different values, they should be entered as a single header, with the value being a comma- separated list of all the desired values as described at https://www.w3.org/Protocols/rfc2616/rfc2616.txt (page 31). Entering two separate headers with the same key in a Create call will cause the first to be overwritten by the second. The maximum number of headers allowed is 100. Fields: acceptedResponseStatusCodes: If present, the check will only pass if the HTTP response status code is in this set of status codes. If empty, the HTTP status code will only pass if the HTTP status code is 200-299. authInfo: The authentication information. Optional when creating an HTTP check; defaults to empty. Do not set both auth_method and auth_info. body: The request body associated with the HTTP POST request. If content_type is URL_ENCODED, the body passed in must be URL-encoded. Users can provide a Content-Length header via the headers field or the API will do so. If the request_method is GET and body is not empty, the API will return an error. The maximum byte size is 1 megabyte.Note: If client libraries aren't used (which performs the conversion automatically) base64 encode your body data since the field is of bytes type. contentType: The content type header to use for the check. The following configurations result in errors: 1. Content type is specified in both the headers field and the content_type field. 2. Request method is GET and content_type is not TYPE_UNSPECIFIED 3. Request method is POST and content_type is TYPE_UNSPECIFIED. 4. Request method is POST and a "Content-Type" header is provided via headers field. The content_type field should be used instead. customContentType: A user provided content type header to use for the check. The invalid configurations outlined in the content_type field apply to custom_content_type, as well as the following: 1. content_type is URL_ENCODED and custom_content_type is set. 2. content_type is USER_PROVIDED and custom_content_type is not set. headers: The list of headers to send as part of the Uptime check request. If two headers have the same key and different values, they should be entered as a single header, with the value being a comma-separated list of all the desired values as described at https://www.w3.org/Protocols/rfc2616/rfc2616.txt (page 31). Entering two separate headers with the same key in a Create call will cause the first to be overwritten by the second. The maximum number of headers allowed is 100. maskHeaders: Boolean specifying whether to encrypt the header information. Encryption should be specified for any headers related to authentication that you do not wish to be seen when retrieving the configuration. The server will be responsible for encrypting the headers. On Get/List calls, if mask_headers is set to true then the headers will be obscured with ******. path: Optional (defaults to "/"). The path to the page against which to run the check. Will be combined with the host (specified within the monitored_resource) and port to construct the full URL. If the provided path does not begin with "/", a "/" will be prepended automatically. pingConfig: Contains information needed to add pings to an HTTP check. port: Optional (defaults to 80 when use_ssl is false, and 443 when use_ssl is true). The TCP port on the HTTP server against which to run the check. Will be combined with host (specified within the monitored_resource) and path to construct the full URL. requestMethod: The HTTP request method to use for the check. If set to METHOD_UNSPECIFIED then request_method defaults to GET. serviceAgentAuthentication: If specified, Uptime will generate and attach an OIDC JWT token for the Monitoring service agent service account as an Authorization header in the HTTP request when probing. useSsl: If true, use HTTPS instead of HTTP to run the check. validateSsl: Boolean specifying whether to include SSL certificate validation as a part of the Uptime check. Only applies to checks where monitored_resource is set to uptime_url. If use_ssl is false, setting validate_ssl to true has no effect. """ class ContentTypeValueValuesEnum(_messages.Enum): r"""The content type header to use for the check. The following configurations result in errors: 1. Content type is specified in both the headers field and the content_type field. 2. Request method is GET and content_type is not TYPE_UNSPECIFIED 3. Request method is POST and content_type is TYPE_UNSPECIFIED. 4. Request method is POST and a "Content-Type" header is provided via headers field. The content_type field should be used instead. Values: TYPE_UNSPECIFIED: No content type specified. URL_ENCODED: body is in URL-encoded form. Equivalent to setting the Content-Type to application/x-www-form-urlencoded in the HTTP request. USER_PROVIDED: body is in custom_content_type form. Equivalent to setting the Content-Type to the contents of custom_content_type in the HTTP request. """ TYPE_UNSPECIFIED = 0 URL_ENCODED = 1 USER_PROVIDED = 2 class RequestMethodValueValuesEnum(_messages.Enum): r"""The HTTP request method to use for the check. If set to METHOD_UNSPECIFIED then request_method defaults to GET. Values: METHOD_UNSPECIFIED: No request method specified. GET: GET request. POST: POST request. """ METHOD_UNSPECIFIED = 0 GET = 1 POST = 2 @encoding.MapUnrecognizedFields('additionalProperties') class HeadersValue(_messages.Message): r"""The list of headers to send as part of the Uptime check request. If two headers have the same key and different values, they should be entered as a single header, with the value being a comma-separated list of all the desired values as described at https://www.w3.org/Protocols/rfc2616/rfc2616.txt (page 31). Entering two separate headers with the same key in a Create call will cause the first to be overwritten by the second. The maximum number of headers allowed is 100. Messages: AdditionalProperty: An additional property for a HeadersValue object. Fields: additionalProperties: Additional properties of type HeadersValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a HeadersValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) acceptedResponseStatusCodes = _messages.MessageField('ResponseStatusCode', 1, repeated=True) authInfo = _messages.MessageField('BasicAuthentication', 2) body = _messages.BytesField(3) contentType = _messages.EnumField('ContentTypeValueValuesEnum', 4) customContentType = _messages.StringField(5) headers = _messages.MessageField('HeadersValue', 6) maskHeaders = _messages.BooleanField(7) path = _messages.StringField(8) pingConfig = _messages.MessageField('PingConfig', 9) port = _messages.IntegerField(10, variant=_messages.Variant.INT32) requestMethod = _messages.EnumField('RequestMethodValueValuesEnum', 11) serviceAgentAuthentication = _messages.MessageField('ServiceAgentAuthentication', 12) useSsl = _messages.BooleanField(13) validateSsl = _messages.BooleanField(14) class InternalChecker(_messages.Message): r"""An internal checker allows Uptime checks to run on private/internal GCP resources. Enums: StateValueValuesEnum: The current operational state of the internal checker. Fields: displayName: The checker's human-readable name. The display name should be unique within a Cloud Monitoring Metrics Scope in order to make it easier to identify; however, uniqueness is not enforced. gcpZone: The GCP zone the Uptime check should egress from. Only respected for internal Uptime checks, where internal_network is specified. name: A unique resource name for this InternalChecker. The format is: projects/[PROJECT_ID_OR_NUMBER]/internalCheckers/[INTERNAL_CHECKER_ID] [PROJECT_ID_OR_NUMBER] is the Cloud Monitoring Metrics Scope project for the Uptime check config associated with the internal checker. network: The GCP VPC network (https://cloud.google.com/vpc/docs/vpc) where the internal resource lives (ex: "default"). peerProjectId: The GCP project ID where the internal checker lives. Not necessary the same as the Metrics Scope project. state: The current operational state of the internal checker. """ class StateValueValuesEnum(_messages.Enum): r"""The current operational state of the internal checker. Values: UNSPECIFIED: An internal checker should never be in the unspecified state. CREATING: The checker is being created, provisioned, and configured. A checker in this state can be returned by ListInternalCheckers or GetInternalChecker, as well as by examining the long running Operation (https://cloud.google.com/apis/design/design_patterns#long_running_ope rations) that created it. RUNNING: The checker is running and available for use. A checker in this state can be returned by ListInternalCheckers or GetInternalChecker as well as by examining the long running Operation (https://cloud.google. com/apis/design/design_patterns#long_running_operations) that created it. If a checker is being torn down, it is neither visible nor usable, so there is no "deleting" or "down" state. """ UNSPECIFIED = 0 CREATING = 1 RUNNING = 2 displayName = _messages.StringField(1) gcpZone = _messages.StringField(2) name = _messages.StringField(3) network = _messages.StringField(4) peerProjectId = _messages.StringField(5) state = _messages.EnumField('StateValueValuesEnum', 6) class IstioCanonicalService(_messages.Message): r"""Canonical service scoped to an Istio mesh. Anthos clusters running ASM >= 1.6.8 will have their services ingested as this type. Fields: canonicalService: The name of the canonical service underlying this service. Corresponds to the destination_canonical_service_name metric label in label in Istio metrics (https://cloud.google.com/monitoring/api/metrics_istio). canonicalServiceNamespace: The namespace of the canonical service underlying this service. Corresponds to the destination_canonical_service_namespace metric label in Istio metrics (https://cloud.google.com/monitoring/api/metrics_istio). meshUid: Identifier for the Istio mesh in which this canonical service is defined. Corresponds to the mesh_uid metric label in Istio metrics (https://cloud.google.com/monitoring/api/metrics_istio). """ canonicalService = _messages.StringField(1) canonicalServiceNamespace = _messages.StringField(2) meshUid = _messages.StringField(3) class JsonPathMatcher(_messages.Message): r"""Information needed to perform a JSONPath content match. Used for ContentMatcherOption::MATCHES_JSON_PATH and ContentMatcherOption::NOT_MATCHES_JSON_PATH. Enums: JsonMatcherValueValuesEnum: The type of JSONPath match that will be applied to the JSON output (ContentMatcher.content) Fields: jsonMatcher: The type of JSONPath match that will be applied to the JSON output (ContentMatcher.content) jsonPath: JSONPath within the response output pointing to the expected ContentMatcher::content to match against. """ class JsonMatcherValueValuesEnum(_messages.Enum): r"""The type of JSONPath match that will be applied to the JSON output (ContentMatcher.content) Values: JSON_PATH_MATCHER_OPTION_UNSPECIFIED: No JSONPath matcher type specified (not valid). EXACT_MATCH: Selects 'exact string' matching. The match succeeds if the content at the json_path within the output is exactly the same as the content string. REGEX_MATCH: Selects regular-expression matching. The match succeeds if the content at the json_path within the output matches the regular expression specified in the content string. """ JSON_PATH_MATCHER_OPTION_UNSPECIFIED = 0 EXACT_MATCH = 1 REGEX_MATCH = 2 jsonMatcher = _messages.EnumField('JsonMatcherValueValuesEnum', 1) jsonPath = _messages.StringField(2) class LabelDescriptor(_messages.Message): r"""A description of a label. Enums: ValueTypeValueValuesEnum: The type of data that can be assigned to the label. Fields: description: A human-readable description for the label. key: The key for this label. The key must meet the following criteria: Does not exceed 100 characters. Matches the following regular expression: [a-zA-Z][a-zA-Z0-9_]* The first character must be an upper- or lower-case letter. The remaining characters must be letters, digits, or underscores. valueType: The type of data that can be assigned to the label. """ class ValueTypeValueValuesEnum(_messages.Enum): r"""The type of data that can be assigned to the label. Values: STRING: A variable-length string, not to exceed 1,024 characters. This is the default value type. BOOL: Boolean; true or false. INT64: A 64-bit signed integer. """ STRING = 0 BOOL = 1 INT64 = 2 description = _messages.StringField(1) key = _messages.StringField(2) valueType = _messages.EnumField('ValueTypeValueValuesEnum', 3) class LabelValue(_messages.Message): r"""A label value. Fields: boolValue: A bool label value. int64Value: An int64 label value. stringValue: A string label value. """ boolValue = _messages.BooleanField(1) int64Value = _messages.IntegerField(2) stringValue = _messages.StringField(3) class LatencyCriteria(_messages.Message): r"""Parameters for a latency threshold SLI. Fields: threshold: Good service is defined to be the count of requests made to this service that return in no more than threshold. """ threshold = _messages.StringField(1) class Linear(_messages.Message): r"""Specifies a linear sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket.There are num_finite_buckets + 2 (= N) buckets. Bucket i has the following boundaries:Upper bound (0 <= i < N-1): offset + (width * i).Lower bound (1 <= i < N): offset + (width * (i - 1)). Fields: numFiniteBuckets: Must be greater than 0. offset: Lower bound of the first bucket. width: Must be greater than 0. """ numFiniteBuckets = _messages.IntegerField(1, variant=_messages.Variant.INT32) offset = _messages.FloatField(2) width = _messages.FloatField(3) class Link(_messages.Message): r"""Links to content such as playbooks, repositories, and other resources. Fields: displayName: A short display name for the link. The display name must not be empty or exceed 63 characters. Example: "playbook". url: The url of a webpage. A url can be templatized by using variables in the path or the query parameters. The total length of a URL should not exceed 2083 characters before and after variable expansion. Example: "https://my_domain.com/playbook?name=${resource.name}" """ displayName = _messages.StringField(1) url = _messages.StringField(2) class ListAlertPoliciesResponse(_messages.Message): r"""The protocol for the ListAlertPolicies response. Fields: alertPolicies: The returned alert policies. nextPageToken: If there might be more results than were returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. totalSize: The total number of alert policies in all pages. This number is only an estimate, and may change in subsequent pages. https://aip.dev/158 """ alertPolicies = _messages.MessageField('AlertPolicy', 1, repeated=True) nextPageToken = _messages.StringField(2) totalSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) class ListAlertsResponse(_messages.Message): r"""The ListAlerts response. Fields: alerts: The list of alerts. nextPageToken: If not empty, indicates that there may be more results that match the request. Use the value in the page_token field in a subsequent request to fetch the next set of results. The token is encrypted and only guaranteed to return correct results for 72 hours after it is created. If empty, all results have been returned. totalSize: The estimated total number of matching results for this query. """ alerts = _messages.MessageField('Alert', 1, repeated=True) nextPageToken = _messages.StringField(2) totalSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) class ListGroupMembersResponse(_messages.Message): r"""The ListGroupMembers response. Fields: members: A set of monitored resources in the group. nextPageToken: If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. totalSize: The total number of elements matching this request. """ members = _messages.MessageField('MonitoredResource', 1, repeated=True) nextPageToken = _messages.StringField(2) totalSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) class ListGroupsResponse(_messages.Message): r"""The ListGroups response. Fields: group: The groups that match the specified filters. nextPageToken: If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. """ group = _messages.MessageField('Group', 1, repeated=True) nextPageToken = _messages.StringField(2) class ListMetricDescriptorsResponse(_messages.Message): r"""The ListMetricDescriptors response. Fields: metricDescriptors: The metric descriptors that are available to the project and that match the value of filter, if present. nextPageToken: If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. """ metricDescriptors = _messages.MessageField('MetricDescriptor', 1, repeated=True) nextPageToken = _messages.StringField(2) class ListMonitoredResourceDescriptorsResponse(_messages.Message): r"""The ListMonitoredResourceDescriptors response. Fields: nextPageToken: If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. resourceDescriptors: The monitored resource descriptors that are available to this project and that match filter, if present. """ nextPageToken = _messages.StringField(1) resourceDescriptors = _messages.MessageField('MonitoredResourceDescriptor', 2, repeated=True) class ListNotificationChannelDescriptorsResponse(_messages.Message): r"""The ListNotificationChannelDescriptors response. Fields: channelDescriptors: The monitored resource descriptors supported for the specified project, optionally filtered. nextPageToken: If not empty, indicates that there may be more results that match the request. Use the value in the page_token field in a subsequent request to fetch the next set of results. If empty, all results have been returned. """ channelDescriptors = _messages.MessageField('NotificationChannelDescriptor', 1, repeated=True) nextPageToken = _messages.StringField(2) class ListNotificationChannelsResponse(_messages.Message): r"""The ListNotificationChannels response. Fields: nextPageToken: If not empty, indicates that there may be more results that match the request. Use the value in the page_token field in a subsequent request to fetch the next set of results. If empty, all results have been returned. notificationChannels: The notification channels defined for the specified project. totalSize: The total number of notification channels in all pages. This number is only an estimate, and may change in subsequent pages. https://aip.dev/158 """ nextPageToken = _messages.StringField(1) notificationChannels = _messages.MessageField('NotificationChannel', 2, repeated=True) totalSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) class ListServiceLevelObjectivesResponse(_messages.Message): r"""The ListServiceLevelObjectives response. Fields: nextPageToken: If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. serviceLevelObjectives: The ServiceLevelObjectives matching the specified filter. """ nextPageToken = _messages.StringField(1) serviceLevelObjectives = _messages.MessageField('ServiceLevelObjective', 2, repeated=True) class ListServicesResponse(_messages.Message): r"""The ListServices response. Fields: nextPageToken: If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. services: The Services matching the specified filter. """ nextPageToken = _messages.StringField(1) services = _messages.MessageField('Service', 2, repeated=True) class ListSnoozesResponse(_messages.Message): r"""The results of a successful ListSnoozes call, containing the matching Snoozes. Fields: nextPageToken: Page token for repeated calls to ListSnoozes, to fetch additional pages of results. If this is empty or missing, there are no more pages. snoozes: Snoozes matching this list call. """ nextPageToken = _messages.StringField(1) snoozes = _messages.MessageField('Snooze', 2, repeated=True) class ListTimeSeriesResponse(_messages.Message): r"""The ListTimeSeries response. Fields: executionErrors: Query execution errors that may have caused the time series data returned to be incomplete. nextPageToken: If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. timeSeries: One or more time series that match the filter included in the request. unit: The unit in which all time_series point values are reported. unit follows the UCUM format for units as seen in https://unitsofmeasure.org/ucum.html. If different time_series have different units (for example, because they come from different metric types, or a unit is absent), then unit will be "{not_a_unit}". unreachable: Cloud regions that were unreachable which may have caused incomplete data to be returned. """ executionErrors = _messages.MessageField('Status', 1, repeated=True) nextPageToken = _messages.StringField(2) timeSeries = _messages.MessageField('TimeSeries', 3, repeated=True) unit = _messages.StringField(4) unreachable = _messages.StringField(5, repeated=True) class ListUptimeCheckConfigsResponse(_messages.Message): r"""The protocol for the ListUptimeCheckConfigs response. Fields: nextPageToken: This field represents the pagination token to retrieve the next page of results. If the value is empty, it means no further results for the request. To retrieve the next page of results, the value of the next_page_token is passed to the subsequent List method call (in the request message's page_token field). totalSize: The total number of Uptime check configurations for the project, irrespective of any pagination. uptimeCheckConfigs: The returned Uptime check configurations. """ nextPageToken = _messages.StringField(1) totalSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) uptimeCheckConfigs = _messages.MessageField('UptimeCheckConfig', 3, repeated=True) class ListUptimeCheckIpsResponse(_messages.Message): r"""The protocol for the ListUptimeCheckIps response. Fields: nextPageToken: This field represents the pagination token to retrieve the next page of results. If the value is empty, it means no further results for the request. To retrieve the next page of results, the value of the next_page_token is passed to the subsequent List method call (in the request message's page_token field). NOTE: this field is not yet implemented uptimeCheckIps: The returned list of IP addresses (including region and location) that the checkers run from. """ nextPageToken = _messages.StringField(1) uptimeCheckIps = _messages.MessageField('UptimeCheckIp', 2, repeated=True) class LogMatch(_messages.Message): r"""A condition type that checks whether a log message in the scoping project (https://cloud.google.com/monitoring/api/v3#project_name) satisfies the given filter. Logs from other projects in the metrics scope are not evaluated. Messages: LabelExtractorsValue: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/d ocs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extract or) for syntax and examples. Fields: filter: Required. A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed. labelExtractors: Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.google.com/logging/d ocs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS.value_extract or) for syntax and examples. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelExtractorsValue(_messages.Message): r"""Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match filter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.Please see the documentation on logs-based metric valueExtractors (https://cloud.goog le.com/logging/docs/reference/v2/rest/v2/projects.metrics#LogMetric.FIELDS .value_extractor) for syntax and examples. Messages: AdditionalProperty: An additional property for a LabelExtractorsValue object. Fields: additionalProperties: Additional properties of type LabelExtractorsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelExtractorsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) filter = _messages.StringField(1) labelExtractors = _messages.MessageField('LabelExtractorsValue', 2) class LogMetadata(_messages.Message): r"""Information about the log for log-based alerts. Messages: ExtractedLabelsValue: The labels extracted from the log. Fields: extractedLabels: The labels extracted from the log. """ @encoding.MapUnrecognizedFields('additionalProperties') class ExtractedLabelsValue(_messages.Message): r"""The labels extracted from the log. Messages: AdditionalProperty: An additional property for a ExtractedLabelsValue object. Fields: additionalProperties: Additional properties of type ExtractedLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ExtractedLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) extractedLabels = _messages.MessageField('ExtractedLabelsValue', 1) class MeshIstio(_messages.Message): r"""Istio service scoped to an Istio mesh. Anthos clusters running ASM < 1.6.8 will have their services ingested as this type. Fields: meshUid: Identifier for the mesh in which this Istio service is defined. Corresponds to the mesh_uid metric label in Istio metrics. serviceName: The name of the Istio service underlying this service. Corresponds to the destination_service_name metric label in Istio metrics. serviceNamespace: The namespace of the Istio service underlying this service. Corresponds to the destination_service_namespace metric label in Istio metrics. """ meshUid = _messages.StringField(1) serviceName = _messages.StringField(2) serviceNamespace = _messages.StringField(3) class Metric(_messages.Message): r"""A specific metric, identified by specifying values for all of the labels of a MetricDescriptor. Messages: LabelsValue: The set of label values that uniquely identify this metric. All labels listed in the MetricDescriptor must be assigned values. Fields: labels: The set of label values that uniquely identify this metric. All labels listed in the MetricDescriptor must be assigned values. type: An existing metric type, see google.api.MetricDescriptor. For example, custom.googleapis.com/invoice/paid/amount. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The set of label values that uniquely identify this metric. All labels listed in the MetricDescriptor must be assigned values. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) labels = _messages.MessageField('LabelsValue', 1) type = _messages.StringField(2) class MetricAbsence(_messages.Message): r"""A condition type that checks that monitored resources are reporting data. The configuration defines a metric and a set of monitored resources. The predicate is considered in violation when a time series for the specified metric of a monitored resource does not include any data in the specified duration. Fields: aggregations: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSer ies/list). It is advisable to use the ListTimeSeries method when debugging this field. duration: Required. The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. filter: Required. A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (http s://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/l ist) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length. trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations. """ aggregations = _messages.MessageField('Aggregation', 1, repeated=True) duration = _messages.StringField(2) filter = _messages.StringField(3) trigger = _messages.MessageField('Trigger', 4) class MetricDescriptor(_messages.Message): r"""Defines a metric type and its schema. Once a metric descriptor is created, deleting or altering it stops data collection and makes the metric type's existing data unusable. Enums: LaunchStageValueValuesEnum: Optional. The launch stage of the metric definition. MetricKindValueValuesEnum: Whether the metric records instantaneous values, changes to a value, etc. Some combinations of metric_kind and value_type might not be supported. ValueTypeValueValuesEnum: Whether the measurement is an integer, a floating-point number, etc. Some combinations of metric_kind and value_type might not be supported. Fields: description: A detailed description of the metric, which can be used in documentation. displayName: A concise name for the metric, which can be displayed in user interfaces. Use sentence case without an ending period, for example "Request count". This field is optional but it is recommended to be set for any metrics associated with user-visible concepts, such as Quota. labels: The set of labels that can be used to describe a specific instance of this metric type. For example, the appengine.googleapis.com/http/server/response_latencies metric type has a label for the HTTP response code, response_code, so you can look at latencies for successful responses or just for responses that failed. launchStage: Optional. The launch stage of the metric definition. metadata: Optional. Metadata which can be used to guide usage of the metric. metricKind: Whether the metric records instantaneous values, changes to a value, etc. Some combinations of metric_kind and value_type might not be supported. monitoredResourceTypes: Read-only. If present, then a time series, which is identified partially by a metric type and a MonitoredResourceDescriptor, that is associated with this metric type can only be associated with one of the monitored resource types listed here. name: The resource name of the metric descriptor. type: The metric type, including its DNS name prefix. The type is not URL- encoded. All user-defined metric types have the DNS name custom.googleapis.com or external.googleapis.com. Metric types should use a natural hierarchical grouping. For example: "custom.googleapis.com/invoice/paid/amount" "external.googleapis.com/prometheus/up" "appengine.googleapis.com/http/server/response_latencies" unit: The units in which the metric value is reported. It is only applicable if the value_type is INT64, DOUBLE, or DISTRIBUTION. The unit defines the representation of the stored metric values.Different systems might scale the values to be more easily displayed (so a value of 0.02kBy might be displayed as 20By, and a value of 3523kBy might be displayed as 3.5MBy). However, if the unit is kBy, then the value of the metric is always in thousands of bytes, no matter how it might be displayed.If you want a custom metric to record the exact number of CPU- seconds used by a job, you can create an INT64 CUMULATIVE metric whose unit is s{CPU} (or equivalently 1s{CPU} or just s). If the job uses 12,005 CPU-seconds, then the value is written as 12005.Alternatively, if you want a custom metric to record data in a more granular way, you can create a DOUBLE CUMULATIVE metric whose unit is ks{CPU}, and then write the value 12.005 (which is 12005/1000), or use Kis{CPU} and write 11.723 (which is 12005/1024).The supported units are a subset of The Unified Code for Units of Measure (https://unitsofmeasure.org/ucum.html) standard:Basic units (UNIT) bit bit By byte s second min minute h hour d day 1 dimensionlessPrefixes (PREFIX) k kilo (10^3) M mega (10^6) G giga (10^9) T tera (10^12) P peta (10^15) E exa (10^18) Z zetta (10^21) Y yotta (10^24) m milli (10^-3) u micro (10^-6) n nano (10^-9) p pico (10^-12) f femto (10^-15) a atto (10^-18) z zepto (10^-21) y yocto (10^-24) Ki kibi (2^10) Mi mebi (2^20) Gi gibi (2^30) Ti tebi (2^40) Pi pebi (2^50)GrammarThe grammar also includes these connectors: / division or ratio (as an infix operator). For examples, kBy/{email} or MiBy/10ms (although you should almost never have /s in a metric unit; rates should always be computed at query time from the underlying cumulative or delta value). . multiplication or composition (as an infix operator). For examples, GBy.d or k{watt}.h.The grammar for a unit is as follows: Expression = Component { "." Component } { "/" Component } ; Component = ( [ PREFIX ] UNIT | "%" ) [ Annotation ] | Annotation | "1" ; Annotation = "{" NAME "}" ; Notes: Annotation is just a comment if it follows a UNIT. If the annotation is used alone, then the unit is equivalent to 1. For examples, {request}/s == 1/s, By{transmitted}/s == By/s. NAME is a sequence of non-blank printable ASCII characters not containing { or }. 1 represents a unitary dimensionless unit (https://en.wikipedia.org/wiki/Dimensionless_quantity) of 1, such as in 1/s. It is typically used when none of the basic units are appropriate. For example, "new users per day" can be represented as 1/d or {new- users}/d (and a metric value 5 would mean "5 new users). Alternatively, "thousands of page views per day" would be represented as 1000/d or k1/d or k{page_views}/d (and a metric value of 5.3 would mean "5300 page views per day"). % represents dimensionless value of 1/100, and annotates values giving a percentage (so the metric values are typically in the range of 0..100, and a metric value 3 means "3 percent"). 10^2.% indicates a metric contains a ratio, typically in the range 0..1, that will be multiplied by 100 and displayed as a percentage (so a metric value 0.03 means "3 percent"). valueType: Whether the measurement is an integer, a floating-point number, etc. Some combinations of metric_kind and value_type might not be supported. """ class LaunchStageValueValuesEnum(_messages.Enum): r"""Optional. The launch stage of the metric definition. Values: LAUNCH_STAGE_UNSPECIFIED: Do not use this default value. UNIMPLEMENTED: The feature is not yet implemented. Users can not use it. PRELAUNCH: Prelaunch features are hidden from users and are only visible internally. EARLY_ACCESS: Early Access features are limited to a closed group of testers. To use these features, you must sign up in advance and sign a Trusted Tester agreement (which includes confidentiality provisions). These features may be unstable, changed in backward-incompatible ways, and are not guaranteed to be released. ALPHA: Alpha is a limited availability test for releases before they are cleared for widespread use. By Alpha, all significant design issues are resolved and we are in the process of verifying functionality. Alpha customers need to apply for access, agree to applicable terms, and have their projects allowlisted. Alpha releases don't have to be feature complete, no SLAs are provided, and there are no technical support obligations, but they will be far enough along that customers can actually use them in test environments or for limited-use tests -- just like they would in normal production cases. BETA: Beta is the point at which we are ready to open a release for any customer to use. There are no SLA or technical support obligations in a Beta release. Products will be complete from a feature perspective, but may have some open outstanding issues. Beta releases are suitable for limited production use cases. GA: GA features are open to all developers and are considered stable and fully qualified for production use. DEPRECATED: Deprecated features are scheduled to be shut down and removed. For more information, see the "Deprecation Policy" section of our Terms of Service (https://cloud.google.com/terms/) and the Google Cloud Platform Subject to the Deprecation Policy (https://cloud.google.com/terms/deprecation) documentation. """ LAUNCH_STAGE_UNSPECIFIED = 0 UNIMPLEMENTED = 1 PRELAUNCH = 2 EARLY_ACCESS = 3 ALPHA = 4 BETA = 5 GA = 6 DEPRECATED = 7 class MetricKindValueValuesEnum(_messages.Enum): r"""Whether the metric records instantaneous values, changes to a value, etc. Some combinations of metric_kind and value_type might not be supported. Values: METRIC_KIND_UNSPECIFIED: Do not use this default value. GAUGE: An instantaneous measurement of a value. DELTA: The change in a value during a time interval. CUMULATIVE: A value accumulated over a time interval. Cumulative measurements in a time series should have the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points. """ METRIC_KIND_UNSPECIFIED = 0 GAUGE = 1 DELTA = 2 CUMULATIVE = 3 class ValueTypeValueValuesEnum(_messages.Enum): r"""Whether the measurement is an integer, a floating-point number, etc. Some combinations of metric_kind and value_type might not be supported. Values: VALUE_TYPE_UNSPECIFIED: Do not use this default value. BOOL: The value is a boolean. This value type can be used only if the metric kind is GAUGE. INT64: The value is a signed 64-bit integer. DOUBLE: The value is a double precision floating point number. STRING: The value is a text string. This value type can be used only if the metric kind is GAUGE. DISTRIBUTION: The value is a Distribution. MONEY: The value is money. """ VALUE_TYPE_UNSPECIFIED = 0 BOOL = 1 INT64 = 2 DOUBLE = 3 STRING = 4 DISTRIBUTION = 5 MONEY = 6 description = _messages.StringField(1) displayName = _messages.StringField(2) labels = _messages.MessageField('LabelDescriptor', 3, repeated=True) launchStage = _messages.EnumField('LaunchStageValueValuesEnum', 4) metadata = _messages.MessageField('MetricDescriptorMetadata', 5) metricKind = _messages.EnumField('MetricKindValueValuesEnum', 6) monitoredResourceTypes = _messages.StringField(7, repeated=True) name = _messages.StringField(8) type = _messages.StringField(9) unit = _messages.StringField(10) valueType = _messages.EnumField('ValueTypeValueValuesEnum', 11) class MetricDescriptorMetadata(_messages.Message): r"""Additional annotations that can be used to guide the usage of a metric. Enums: LaunchStageValueValuesEnum: Deprecated. Must use the MetricDescriptor.launch_stage instead. TimeSeriesResourceHierarchyLevelValueListEntryValuesEnum: Fields: ingestDelay: The delay of data points caused by ingestion. Data points older than this age are guaranteed to be ingested and available to be read, excluding data loss due to errors. launchStage: Deprecated. Must use the MetricDescriptor.launch_stage instead. samplePeriod: The sampling period of metric data points. For metrics which are written periodically, consecutive data points are stored at this time interval, excluding data loss due to errors. Metrics with a higher granularity have a smaller sampling period. timeSeriesResourceHierarchyLevel: The scope of the timeseries data of the metric. """ class LaunchStageValueValuesEnum(_messages.Enum): r"""Deprecated. Must use the MetricDescriptor.launch_stage instead. Values: LAUNCH_STAGE_UNSPECIFIED: Do not use this default value. UNIMPLEMENTED: The feature is not yet implemented. Users can not use it. PRELAUNCH: Prelaunch features are hidden from users and are only visible internally. EARLY_ACCESS: Early Access features are limited to a closed group of testers. To use these features, you must sign up in advance and sign a Trusted Tester agreement (which includes confidentiality provisions). These features may be unstable, changed in backward-incompatible ways, and are not guaranteed to be released. ALPHA: Alpha is a limited availability test for releases before they are cleared for widespread use. By Alpha, all significant design issues are resolved and we are in the process of verifying functionality. Alpha customers need to apply for access, agree to applicable terms, and have their projects allowlisted. Alpha releases don't have to be feature complete, no SLAs are provided, and there are no technical support obligations, but they will be far enough along that customers can actually use them in test environments or for limited-use tests -- just like they would in normal production cases. BETA: Beta is the point at which we are ready to open a release for any customer to use. There are no SLA or technical support obligations in a Beta release. Products will be complete from a feature perspective, but may have some open outstanding issues. Beta releases are suitable for limited production use cases. GA: GA features are open to all developers and are considered stable and fully qualified for production use. DEPRECATED: Deprecated features are scheduled to be shut down and removed. For more information, see the "Deprecation Policy" section of our Terms of Service (https://cloud.google.com/terms/) and the Google Cloud Platform Subject to the Deprecation Policy (https://cloud.google.com/terms/deprecation) documentation. """ LAUNCH_STAGE_UNSPECIFIED = 0 UNIMPLEMENTED = 1 PRELAUNCH = 2 EARLY_ACCESS = 3 ALPHA = 4 BETA = 5 GA = 6 DEPRECATED = 7 class TimeSeriesResourceHierarchyLevelValueListEntryValuesEnum(_messages.Enum): r"""TimeSeriesResourceHierarchyLevelValueListEntryValuesEnum enum type. Values: TIME_SERIES_RESOURCE_HIERARCHY_LEVEL_UNSPECIFIED: Do not use this default value. PROJECT: Scopes a metric to a project. ORGANIZATION: Scopes a metric to an organization. FOLDER: Scopes a metric to a folder. """ TIME_SERIES_RESOURCE_HIERARCHY_LEVEL_UNSPECIFIED = 0 PROJECT = 1 ORGANIZATION = 2 FOLDER = 3 ingestDelay = _messages.StringField(1) launchStage = _messages.EnumField('LaunchStageValueValuesEnum', 2) samplePeriod = _messages.StringField(3) timeSeriesResourceHierarchyLevel = _messages.EnumField('TimeSeriesResourceHierarchyLevelValueListEntryValuesEnum', 4, repeated=True) class MetricRange(_messages.Message): r"""A MetricRange is used when each window is good when the value x of a single TimeSeries satisfies range.min <= x <= range.max. The provided TimeSeries must have ValueType = INT64 or ValueType = DOUBLE and MetricKind = GAUGE. Fields: range: Range of values considered "good." For a one-sided range, set one bound to an infinite value. timeSeries: A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) specifying the TimeSeries to use for evaluating window quality. """ range = _messages.MessageField('GoogleMonitoringV3Range', 1) timeSeries = _messages.StringField(2) class MetricThreshold(_messages.Message): r"""A condition type that compares a collection of time series against a threshold. Enums: ComparisonValueValuesEnum: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right- hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently. EvaluationMissingDataValueValuesEnum: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. To use this control, the value of the duration field must be greater than or equal to 60 seconds. Fields: aggregations: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSer ies/list). It is advisable to use the ListTimeSeries method when debugging this field. comparison: The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right- hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently. denominatorAggregations: Specifies the alignment of data points in individual time series selected by denominatorFilter as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources).When computing ratios, the aggregations and denominator_aggregations fields must use the same alignment period and produce time series that have the same periodicity and labels. denominatorFilter: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length. duration: Required. The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly. evaluationMissingData: A condition control that determines how metric- threshold conditions are evaluated when data stops arriving. To use this control, the value of the duration field must be greater than or equal to 60 seconds. filter: Required. A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies which time series should be compared with the threshold.The filter is similar to the one that is specified in the ListTimeSeries request (http s://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/l ist) (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must not exceed 2048 Unicode characters in length. forecastOptions: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold. thresholdValue: A value against which to compare the time series. trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified. """ class ComparisonValueValuesEnum(_messages.Enum): r"""The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by threshold_value). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.Only COMPARISON_LT and COMPARISON_GT are supported currently. Values: COMPARISON_UNSPECIFIED: No ordering relationship is specified. COMPARISON_GT: True if the left argument is greater than the right argument. COMPARISON_GE: True if the left argument is greater than or equal to the right argument. COMPARISON_LT: True if the left argument is less than the right argument. COMPARISON_LE: True if the left argument is less than or equal to the right argument. COMPARISON_EQ: True if the left argument is equal to the right argument. COMPARISON_NE: True if the left argument is not equal to the right argument. """ COMPARISON_UNSPECIFIED = 0 COMPARISON_GT = 1 COMPARISON_GE = 2 COMPARISON_LT = 3 COMPARISON_LE = 4 COMPARISON_EQ = 5 COMPARISON_NE = 6 class EvaluationMissingDataValueValuesEnum(_messages.Enum): r"""A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. To use this control, the value of the duration field must be greater than or equal to 60 seconds. Values: EVALUATION_MISSING_DATA_UNSPECIFIED: An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP. EVALUATION_MISSING_DATA_INACTIVE: If there is no data to evaluate the condition, then evaluate the condition as false. EVALUATION_MISSING_DATA_ACTIVE: If there is no data to evaluate the condition, then evaluate the condition as true. EVALUATION_MISSING_DATA_NO_OP: Do not evaluate the condition to any value if there is no data. """ EVALUATION_MISSING_DATA_UNSPECIFIED = 0 EVALUATION_MISSING_DATA_INACTIVE = 1 EVALUATION_MISSING_DATA_ACTIVE = 2 EVALUATION_MISSING_DATA_NO_OP = 3 aggregations = _messages.MessageField('Aggregation', 1, repeated=True) comparison = _messages.EnumField('ComparisonValueValuesEnum', 2) denominatorAggregations = _messages.MessageField('Aggregation', 3, repeated=True) denominatorFilter = _messages.StringField(4) duration = _messages.StringField(5) evaluationMissingData = _messages.EnumField('EvaluationMissingDataValueValuesEnum', 6) filter = _messages.StringField(7) forecastOptions = _messages.MessageField('ForecastOptions', 8) thresholdValue = _messages.FloatField(9) trigger = _messages.MessageField('Trigger', 10) class Minutes(_messages.Message): r"""Used to schedule the query to run every so many minutes. Fields: periodicity: Required. Number of minutes between runs. The interval must be greater than or equal to 5 minutes and less than or equal to 1440 minutes. """ periodicity = _messages.IntegerField(1, variant=_messages.Variant.INT32) class MonitoredResource(_messages.Message): r"""An object representing a resource that can be used for monitoring, logging, billing, or other purposes. Examples include virtual machine instances, databases, and storage devices such as disks. The type field identifies a MonitoredResourceDescriptor object that describes the resource's schema. Information in the labels field identifies the actual resource and its attributes according to the schema. For example, a particular Compute Engine VM instance could be represented by the following object, because the MonitoredResourceDescriptor for "gce_instance" has labels "project_id", "instance_id" and "zone": { "type": "gce_instance", "labels": { "project_id": "my-project", "instance_id": "12345678901234", "zone": "us-central1-a" }} Messages: LabelsValue: Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone". Fields: labels: Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone". type: Required. The monitored resource type. This field must match the type field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is gce_instance. For a list of types, see Monitoring resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list). """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels "project_id", "instance_id", and "zone". Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) labels = _messages.MessageField('LabelsValue', 1) type = _messages.StringField(2) class MonitoredResourceDescriptor(_messages.Message): r"""An object that describes the schema of a MonitoredResource object using a type name and a set of labels. For example, the monitored resource descriptor for Google Compute Engine VM instances has a type of "gce_instance" and specifies the use of the labels "instance_id" and "zone" to identify particular VM instances.Different APIs can support different monitored resource types. APIs generally provide a list method that returns the monitored resource descriptors used by the API. Enums: LaunchStageValueValuesEnum: Optional. The launch stage of the monitored resource definition. Fields: description: Optional. A detailed description of the monitored resource type that might be used in documentation. displayName: Optional. A concise name for the monitored resource type that might be displayed in user interfaces. It should be a Title Cased Noun Phrase, without any article or other determiners. For example, "Google Cloud SQL Database". labels: Required. A set of labels used to describe instances of this monitored resource type. For example, an individual Google Cloud SQL database is identified by values for the labels "database_id" and "zone". launchStage: Optional. The launch stage of the monitored resource definition. name: Optional. The resource name of the monitored resource descriptor: "projects/{project_id}/monitoredResourceDescriptors/{type}" where {type} is the value of the type field in this object and {project_id} is a project ID that provides API-specific context for accessing the type. APIs that do not use project information can use the resource name format "monitoredResourceDescriptors/{type}". type: Required. The monitored resource type. For example, the type "cloudsql_database" represents databases in Google Cloud SQL. For a list of types, see Monitored resource types (https://cloud.google.com/monitoring/api/resources) and Logging resource types (https://cloud.google.com/logging/docs/api/v2/resource-list). """ class LaunchStageValueValuesEnum(_messages.Enum): r"""Optional. The launch stage of the monitored resource definition. Values: LAUNCH_STAGE_UNSPECIFIED: Do not use this default value. UNIMPLEMENTED: The feature is not yet implemented. Users can not use it. PRELAUNCH: Prelaunch features are hidden from users and are only visible internally. EARLY_ACCESS: Early Access features are limited to a closed group of testers. To use these features, you must sign up in advance and sign a Trusted Tester agreement (which includes confidentiality provisions). These features may be unstable, changed in backward-incompatible ways, and are not guaranteed to be released. ALPHA: Alpha is a limited availability test for releases before they are cleared for widespread use. By Alpha, all significant design issues are resolved and we are in the process of verifying functionality. Alpha customers need to apply for access, agree to applicable terms, and have their projects allowlisted. Alpha releases don't have to be feature complete, no SLAs are provided, and there are no technical support obligations, but they will be far enough along that customers can actually use them in test environments or for limited-use tests -- just like they would in normal production cases. BETA: Beta is the point at which we are ready to open a release for any customer to use. There are no SLA or technical support obligations in a Beta release. Products will be complete from a feature perspective, but may have some open outstanding issues. Beta releases are suitable for limited production use cases. GA: GA features are open to all developers and are considered stable and fully qualified for production use. DEPRECATED: Deprecated features are scheduled to be shut down and removed. For more information, see the "Deprecation Policy" section of our Terms of Service (https://cloud.google.com/terms/) and the Google Cloud Platform Subject to the Deprecation Policy (https://cloud.google.com/terms/deprecation) documentation. """ LAUNCH_STAGE_UNSPECIFIED = 0 UNIMPLEMENTED = 1 PRELAUNCH = 2 EARLY_ACCESS = 3 ALPHA = 4 BETA = 5 GA = 6 DEPRECATED = 7 description = _messages.StringField(1) displayName = _messages.StringField(2) labels = _messages.MessageField('LabelDescriptor', 3, repeated=True) launchStage = _messages.EnumField('LaunchStageValueValuesEnum', 4) name = _messages.StringField(5) type = _messages.StringField(6) class MonitoredResourceMetadata(_messages.Message): r"""Auxiliary metadata for a MonitoredResource object. MonitoredResource objects contain the minimum set of information to uniquely identify a monitored resource instance. There is some other useful auxiliary metadata. Monitoring and Logging use an ingestion pipeline to extract metadata for cloud resources of all types, and store the metadata in this message. Messages: SystemLabelsValue: Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false } UserLabelsValue: Output only. A map of user-defined metadata labels. Fields: systemLabels: Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false } userLabels: Output only. A map of user-defined metadata labels. """ @encoding.MapUnrecognizedFields('additionalProperties') class SystemLabelsValue(_messages.Message): r"""Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false } Messages: AdditionalProperty: An additional property for a SystemLabelsValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a SystemLabelsValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class UserLabelsValue(_messages.Message): r"""Output only. A map of user-defined metadata labels. Messages: AdditionalProperty: An additional property for a UserLabelsValue object. Fields: additionalProperties: Additional properties of type UserLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a UserLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) systemLabels = _messages.MessageField('SystemLabelsValue', 1) userLabels = _messages.MessageField('UserLabelsValue', 2) class MonitoringFoldersTimeSeriesListRequest(_messages.Message): r"""A MonitoringFoldersTimeSeriesListRequest object. Enums: AggregationCrossSeriesReducerValueValuesEnum: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. AggregationPerSeriesAlignerValueValuesEnum: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. SecondaryAggregationCrossSeriesReducerValueValuesEnum: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. SecondaryAggregationPerSeriesAlignerValueValuesEnum: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. ViewValueValuesEnum: Required. Specifies which information is returned about the time series. Fields: aggregation_alignmentPeriod: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per- series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies. aggregation_crossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. aggregation_groupByFields: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored. aggregation_perSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. filter: Required. A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that specifies which time series should be returned. The filter must specify a single metric type, and can additionally specify metric labels and other information. For example: metric.type = "compute.googleapis.com/instance/cpu/usage_time" AND metric.labels.instance_name = "my-instance-name" interval_endTime: Required. The end of the time interval. interval_startTime: 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. name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name), organization or folder on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] organizations/[ORGANIZATION_ID] folders/[FOLDER_ID] orderBy: Unsupported: must be left blank. The points in each time series are currently returned in reverse time order (most recent to oldest). pageSize: A positive number that is the maximum number of results to return. If page_size is empty or more than 100,000 results, the effective page_size is 100,000 results. If view is set to FULL, this is the maximum number of Points returned. If view is set to HEADERS, this is the maximum number of TimeSeries returned. pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. secondaryAggregation_alignmentPeriod: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies. secondaryAggregation_crossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. secondaryAggregation_groupByFields: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored. secondaryAggregation_perSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. view: Required. Specifies which information is returned about the time series. """ class AggregationCrossSeriesReducerValueValuesEnum(_messages.Enum): r"""The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Values: REDUCE_NONE: No cross-time series reduction. The output of the Aligner is returned. REDUCE_MEAN: Reduce by computing the mean value 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. REDUCE_MIN: Reduce by computing the minimum value 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. REDUCE_MAX: Reduce by computing the maximum value 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. REDUCE_SUM: 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. REDUCE_STDDEV: 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. REDUCE_COUNT: Reduce by computing the number 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. REDUCE_COUNT_TRUE: Reduce by computing the number 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. REDUCE_COUNT_FALSE: Reduce by computing the number 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. REDUCE_FRACTION_TRUE: Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_95: Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_50: Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_05: Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/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. """ 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 = 8 REDUCE_FRACTION_TRUE = 9 REDUCE_PERCENTILE_99 = 10 REDUCE_PERCENTILE_95 = 11 REDUCE_PERCENTILE_50 = 12 REDUCE_PERCENTILE_05 = 13 class AggregationPerSeriesAlignerValueValuesEnum(_messages.Enum): r"""An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Values: ALIGN_NONE: No alignment. Raw data is returned. Not valid if cross- series reduction is requested. The value_type of the result is the same as the value_type of the input. ALIGN_DELTA: Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input. ALIGN_RATE: Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead. ALIGN_INTERPOLATE: Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_NEXT_OLDER: Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MIN: Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MAX: Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MEAN: Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE. ALIGN_COUNT: Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64. ALIGN_SUM: Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_STDDEV: Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE. ALIGN_COUNT_TRUE: Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_COUNT_FALSE: Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_FRACTION_TRUE: Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. ALIGN_PERCENTILE_99: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_95: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_50: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_05: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENT_CHANGE: Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. 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. """ ALIGN_NONE = 0 ALIGN_DELTA = 1 ALIGN_RATE = 2 ALIGN_INTERPOLATE = 3 ALIGN_NEXT_OLDER = 4 ALIGN_MIN = 5 ALIGN_MAX = 6 ALIGN_MEAN = 7 ALIGN_COUNT = 8 ALIGN_SUM = 9 ALIGN_STDDEV = 10 ALIGN_COUNT_TRUE = 11 ALIGN_COUNT_FALSE = 12 ALIGN_FRACTION_TRUE = 13 ALIGN_PERCENTILE_99 = 14 ALIGN_PERCENTILE_95 = 15 ALIGN_PERCENTILE_50 = 16 ALIGN_PERCENTILE_05 = 17 ALIGN_PERCENT_CHANGE = 18 class SecondaryAggregationCrossSeriesReducerValueValuesEnum(_messages.Enum): r"""The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Values: REDUCE_NONE: No cross-time series reduction. The output of the Aligner is returned. REDUCE_MEAN: Reduce by computing the mean value 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. REDUCE_MIN: Reduce by computing the minimum value 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. REDUCE_MAX: Reduce by computing the maximum value 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. REDUCE_SUM: 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. REDUCE_STDDEV: 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. REDUCE_COUNT: Reduce by computing the number 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. REDUCE_COUNT_TRUE: Reduce by computing the number 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. REDUCE_COUNT_FALSE: Reduce by computing the number 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. REDUCE_FRACTION_TRUE: Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_95: Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_50: Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_05: Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/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. """ 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 = 8 REDUCE_FRACTION_TRUE = 9 REDUCE_PERCENTILE_99 = 10 REDUCE_PERCENTILE_95 = 11 REDUCE_PERCENTILE_50 = 12 REDUCE_PERCENTILE_05 = 13 class SecondaryAggregationPerSeriesAlignerValueValuesEnum(_messages.Enum): r"""An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Values: ALIGN_NONE: No alignment. Raw data is returned. Not valid if cross- series reduction is requested. The value_type of the result is the same as the value_type of the input. ALIGN_DELTA: Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input. ALIGN_RATE: Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead. ALIGN_INTERPOLATE: Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_NEXT_OLDER: Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MIN: Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MAX: Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MEAN: Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE. ALIGN_COUNT: Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64. ALIGN_SUM: Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_STDDEV: Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE. ALIGN_COUNT_TRUE: Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_COUNT_FALSE: Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_FRACTION_TRUE: Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. ALIGN_PERCENTILE_99: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_95: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_50: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_05: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENT_CHANGE: Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. 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. """ ALIGN_NONE = 0 ALIGN_DELTA = 1 ALIGN_RATE = 2 ALIGN_INTERPOLATE = 3 ALIGN_NEXT_OLDER = 4 ALIGN_MIN = 5 ALIGN_MAX = 6 ALIGN_MEAN = 7 ALIGN_COUNT = 8 ALIGN_SUM = 9 ALIGN_STDDEV = 10 ALIGN_COUNT_TRUE = 11 ALIGN_COUNT_FALSE = 12 ALIGN_FRACTION_TRUE = 13 ALIGN_PERCENTILE_99 = 14 ALIGN_PERCENTILE_95 = 15 ALIGN_PERCENTILE_50 = 16 ALIGN_PERCENTILE_05 = 17 ALIGN_PERCENT_CHANGE = 18 class ViewValueValuesEnum(_messages.Enum): r"""Required. Specifies which information is returned about the time series. Values: FULL: Returns the identity of the metric(s), the time series, and the time series data. HEADERS: Returns the identity of the metric and the time series resource, but not the time series data. """ FULL = 0 HEADERS = 1 aggregation_alignmentPeriod = _messages.StringField(1) aggregation_crossSeriesReducer = _messages.EnumField('AggregationCrossSeriesReducerValueValuesEnum', 2) aggregation_groupByFields = _messages.StringField(3, repeated=True) aggregation_perSeriesAligner = _messages.EnumField('AggregationPerSeriesAlignerValueValuesEnum', 4) filter = _messages.StringField(5) interval_endTime = _messages.StringField(6) interval_startTime = _messages.StringField(7) name = _messages.StringField(8, required=True) orderBy = _messages.StringField(9) pageSize = _messages.IntegerField(10, variant=_messages.Variant.INT32) pageToken = _messages.StringField(11) secondaryAggregation_alignmentPeriod = _messages.StringField(12) secondaryAggregation_crossSeriesReducer = _messages.EnumField('SecondaryAggregationCrossSeriesReducerValueValuesEnum', 13) secondaryAggregation_groupByFields = _messages.StringField(14, repeated=True) secondaryAggregation_perSeriesAligner = _messages.EnumField('SecondaryAggregationPerSeriesAlignerValueValuesEnum', 15) view = _messages.EnumField('ViewValueValuesEnum', 16) class MonitoringOrganizationsTimeSeriesListRequest(_messages.Message): r"""A MonitoringOrganizationsTimeSeriesListRequest object. Enums: AggregationCrossSeriesReducerValueValuesEnum: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. AggregationPerSeriesAlignerValueValuesEnum: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. SecondaryAggregationCrossSeriesReducerValueValuesEnum: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. SecondaryAggregationPerSeriesAlignerValueValuesEnum: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. ViewValueValuesEnum: Required. Specifies which information is returned about the time series. Fields: aggregation_alignmentPeriod: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per- series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies. aggregation_crossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. aggregation_groupByFields: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored. aggregation_perSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. filter: Required. A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that specifies which time series should be returned. The filter must specify a single metric type, and can additionally specify metric labels and other information. For example: metric.type = "compute.googleapis.com/instance/cpu/usage_time" AND metric.labels.instance_name = "my-instance-name" interval_endTime: Required. The end of the time interval. interval_startTime: 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. name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name), organization or folder on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] organizations/[ORGANIZATION_ID] folders/[FOLDER_ID] orderBy: Unsupported: must be left blank. The points in each time series are currently returned in reverse time order (most recent to oldest). pageSize: A positive number that is the maximum number of results to return. If page_size is empty or more than 100,000 results, the effective page_size is 100,000 results. If view is set to FULL, this is the maximum number of Points returned. If view is set to HEADERS, this is the maximum number of TimeSeries returned. pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. secondaryAggregation_alignmentPeriod: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies. secondaryAggregation_crossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. secondaryAggregation_groupByFields: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored. secondaryAggregation_perSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. view: Required. Specifies which information is returned about the time series. """ class AggregationCrossSeriesReducerValueValuesEnum(_messages.Enum): r"""The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Values: REDUCE_NONE: No cross-time series reduction. The output of the Aligner is returned. REDUCE_MEAN: Reduce by computing the mean value 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. REDUCE_MIN: Reduce by computing the minimum value 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. REDUCE_MAX: Reduce by computing the maximum value 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. REDUCE_SUM: 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. REDUCE_STDDEV: 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. REDUCE_COUNT: Reduce by computing the number 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. REDUCE_COUNT_TRUE: Reduce by computing the number 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. REDUCE_COUNT_FALSE: Reduce by computing the number 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. REDUCE_FRACTION_TRUE: Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_95: Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_50: Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_05: Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/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. """ 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 = 8 REDUCE_FRACTION_TRUE = 9 REDUCE_PERCENTILE_99 = 10 REDUCE_PERCENTILE_95 = 11 REDUCE_PERCENTILE_50 = 12 REDUCE_PERCENTILE_05 = 13 class AggregationPerSeriesAlignerValueValuesEnum(_messages.Enum): r"""An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Values: ALIGN_NONE: No alignment. Raw data is returned. Not valid if cross- series reduction is requested. The value_type of the result is the same as the value_type of the input. ALIGN_DELTA: Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input. ALIGN_RATE: Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead. ALIGN_INTERPOLATE: Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_NEXT_OLDER: Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MIN: Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MAX: Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MEAN: Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE. ALIGN_COUNT: Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64. ALIGN_SUM: Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_STDDEV: Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE. ALIGN_COUNT_TRUE: Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_COUNT_FALSE: Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_FRACTION_TRUE: Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. ALIGN_PERCENTILE_99: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_95: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_50: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_05: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENT_CHANGE: Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. 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. """ ALIGN_NONE = 0 ALIGN_DELTA = 1 ALIGN_RATE = 2 ALIGN_INTERPOLATE = 3 ALIGN_NEXT_OLDER = 4 ALIGN_MIN = 5 ALIGN_MAX = 6 ALIGN_MEAN = 7 ALIGN_COUNT = 8 ALIGN_SUM = 9 ALIGN_STDDEV = 10 ALIGN_COUNT_TRUE = 11 ALIGN_COUNT_FALSE = 12 ALIGN_FRACTION_TRUE = 13 ALIGN_PERCENTILE_99 = 14 ALIGN_PERCENTILE_95 = 15 ALIGN_PERCENTILE_50 = 16 ALIGN_PERCENTILE_05 = 17 ALIGN_PERCENT_CHANGE = 18 class SecondaryAggregationCrossSeriesReducerValueValuesEnum(_messages.Enum): r"""The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Values: REDUCE_NONE: No cross-time series reduction. The output of the Aligner is returned. REDUCE_MEAN: Reduce by computing the mean value 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. REDUCE_MIN: Reduce by computing the minimum value 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. REDUCE_MAX: Reduce by computing the maximum value 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. REDUCE_SUM: 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. REDUCE_STDDEV: 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. REDUCE_COUNT: Reduce by computing the number 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. REDUCE_COUNT_TRUE: Reduce by computing the number 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. REDUCE_COUNT_FALSE: Reduce by computing the number 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. REDUCE_FRACTION_TRUE: Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_95: Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_50: Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_05: Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/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. """ 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 = 8 REDUCE_FRACTION_TRUE = 9 REDUCE_PERCENTILE_99 = 10 REDUCE_PERCENTILE_95 = 11 REDUCE_PERCENTILE_50 = 12 REDUCE_PERCENTILE_05 = 13 class SecondaryAggregationPerSeriesAlignerValueValuesEnum(_messages.Enum): r"""An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Values: ALIGN_NONE: No alignment. Raw data is returned. Not valid if cross- series reduction is requested. The value_type of the result is the same as the value_type of the input. ALIGN_DELTA: Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input. ALIGN_RATE: Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead. ALIGN_INTERPOLATE: Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_NEXT_OLDER: Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MIN: Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MAX: Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MEAN: Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE. ALIGN_COUNT: Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64. ALIGN_SUM: Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_STDDEV: Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE. ALIGN_COUNT_TRUE: Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_COUNT_FALSE: Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_FRACTION_TRUE: Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. ALIGN_PERCENTILE_99: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_95: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_50: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_05: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENT_CHANGE: Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. 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. """ ALIGN_NONE = 0 ALIGN_DELTA = 1 ALIGN_RATE = 2 ALIGN_INTERPOLATE = 3 ALIGN_NEXT_OLDER = 4 ALIGN_MIN = 5 ALIGN_MAX = 6 ALIGN_MEAN = 7 ALIGN_COUNT = 8 ALIGN_SUM = 9 ALIGN_STDDEV = 10 ALIGN_COUNT_TRUE = 11 ALIGN_COUNT_FALSE = 12 ALIGN_FRACTION_TRUE = 13 ALIGN_PERCENTILE_99 = 14 ALIGN_PERCENTILE_95 = 15 ALIGN_PERCENTILE_50 = 16 ALIGN_PERCENTILE_05 = 17 ALIGN_PERCENT_CHANGE = 18 class ViewValueValuesEnum(_messages.Enum): r"""Required. Specifies which information is returned about the time series. Values: FULL: Returns the identity of the metric(s), the time series, and the time series data. HEADERS: Returns the identity of the metric and the time series resource, but not the time series data. """ FULL = 0 HEADERS = 1 aggregation_alignmentPeriod = _messages.StringField(1) aggregation_crossSeriesReducer = _messages.EnumField('AggregationCrossSeriesReducerValueValuesEnum', 2) aggregation_groupByFields = _messages.StringField(3, repeated=True) aggregation_perSeriesAligner = _messages.EnumField('AggregationPerSeriesAlignerValueValuesEnum', 4) filter = _messages.StringField(5) interval_endTime = _messages.StringField(6) interval_startTime = _messages.StringField(7) name = _messages.StringField(8, required=True) orderBy = _messages.StringField(9) pageSize = _messages.IntegerField(10, variant=_messages.Variant.INT32) pageToken = _messages.StringField(11) secondaryAggregation_alignmentPeriod = _messages.StringField(12) secondaryAggregation_crossSeriesReducer = _messages.EnumField('SecondaryAggregationCrossSeriesReducerValueValuesEnum', 13) secondaryAggregation_groupByFields = _messages.StringField(14, repeated=True) secondaryAggregation_perSeriesAligner = _messages.EnumField('SecondaryAggregationPerSeriesAlignerValueValuesEnum', 15) view = _messages.EnumField('ViewValueValuesEnum', 16) class MonitoringProjectsAlertPoliciesCreateRequest(_messages.Message): r"""A MonitoringProjectsAlertPoliciesCreateRequest object. Fields: alertPolicy: A AlertPolicy resource to be passed as the request body. name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) in which to create the alerting policy. The format is: projects/[PROJECT_ID_OR_NUMBER] Note that this field names the parent container in which the alerting policy will be written, not the name of the created policy. |name| must be a host project of a Metrics Scope, otherwise INVALID_ARGUMENT error will return. The alerting policy that is returned will have a name that contains a normalized representation of this name as a prefix but adds a suffix of the form /alertPolicies/[ALERT_POLICY_ID], identifying the policy in the container. """ alertPolicy = _messages.MessageField('AlertPolicy', 1) name = _messages.StringField(2, required=True) class MonitoringProjectsAlertPoliciesDeleteRequest(_messages.Message): r"""A MonitoringProjectsAlertPoliciesDeleteRequest object. Fields: name: Required. The alerting policy to delete. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] For more information, see AlertPolicy. """ name = _messages.StringField(1, required=True) class MonitoringProjectsAlertPoliciesGetRequest(_messages.Message): r"""A MonitoringProjectsAlertPoliciesGetRequest object. Fields: name: Required. The alerting policy to retrieve. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] """ name = _messages.StringField(1, required=True) class MonitoringProjectsAlertPoliciesListRequest(_messages.Message): r"""A MonitoringProjectsAlertPoliciesListRequest object. Fields: filter: Optional. If provided, this field specifies the criteria that must be met by alert policies to be included in the response.For more details, see sorting and filtering (https://cloud.google.com/monitoring/api/v3/sorting-and-filtering). name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) whose alert policies are to be listed. The format is: projects/[PROJECT_ID_OR_NUMBER] Note that this field names the parent container in which the alerting policies to be listed are stored. To retrieve a single alerting policy by name, use the GetAlertPolicy operation, instead. orderBy: Optional. A comma-separated list of fields by which to sort the result. Supports the same set of field references as the filter field. Entries can be prefixed with a minus sign to sort by the field in descending order.For more details, see sorting and filtering (https://cloud.google.com/monitoring/api/v3/sorting-and-filtering). pageSize: Optional. The maximum number of results to return in a single response. pageToken: Optional. If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return more results from the previous method call. """ filter = _messages.StringField(1) name = _messages.StringField(2, required=True) orderBy = _messages.StringField(3) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) class MonitoringProjectsAlertPoliciesPatchRequest(_messages.Message): r"""A MonitoringProjectsAlertPoliciesPatchRequest object. Fields: alertPolicy: A AlertPolicy resource to be passed as the request body. name: Identifier. Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request. updateMask: Optional. A list of alerting policy field names. If this field is not empty, each listed field in the existing alerting policy is set to the value of the corresponding field in the supplied policy (alert_policy), or to the field's default value if the field is not in the supplied alerting policy. Fields not listed retain their previous value.Examples of valid field masks include display_name, documentation, documentation.content, documentation.mime_type, user_labels, user_label.nameofkey, enabled, conditions, combiner, etc.If this field is empty, then the supplied alerting policy replaces the existing policy. It is the same as deleting the existing policy and adding the supplied policy, except for the following: The new policy will have the same [ALERT_POLICY_ID] as the former policy. This gives you continuity with the former policy in your notifications and incidents. Conditions in the new policy will keep their former [CONDITION_ID] if the supplied condition includes the name field with that [CONDITION_ID]. If the supplied condition omits the name field, then a new [CONDITION_ID] is created. """ alertPolicy = _messages.MessageField('AlertPolicy', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class MonitoringProjectsAlertsGetRequest(_messages.Message): r"""A MonitoringProjectsAlertsGetRequest object. Fields: name: Required. The name of the alert.The format is: projects/[PROJECT_ID_OR_NUMBER]/alerts/[ALERT_ID] The [ALERT_ID] is a system-assigned unique identifier for the alert. """ name = _messages.StringField(1, required=True) class MonitoringProjectsAlertsListRequest(_messages.Message): r"""A MonitoringProjectsAlertsListRequest object. Fields: filter: Optional. An alert is returned if there is a match on any fields belonging to the alert or its subfields. orderBy: Optional. A comma-separated list of fields in Alert to use for sorting. The default sort direction is ascending. To specify descending order for a field, add a desc modifier. The following fields are supported: open_time close_timeFor example, close_time desc, open_time will return the alerts closed most recently, with ties broken in the order of older alerts listed first.If the field is not set, the results are sorted by open_time desc. pageSize: Optional. The maximum number of results to return in a single response. If not set to a positive number, at most 50 alerts will be returned. The maximum value is 1000; values above 1000 will be coerced to 1000. pageToken: Optional. If non-empty, page_token must contain a value returned as the next_page_token in a previous response to request the next set of results. parent: Required. The name of the project to list alerts for. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class MonitoringProjectsCollectdTimeSeriesCreateRequest(_messages.Message): r"""A MonitoringProjectsCollectdTimeSeriesCreateRequest object. Fields: createCollectdTimeSeriesRequest: A CreateCollectdTimeSeriesRequest resource to be passed as the request body. name: The project (https://cloud.google.com/monitoring/api/v3#project_name) in which to create the time series. The format is: projects/[PROJECT_ID_OR_NUMBER] """ createCollectdTimeSeriesRequest = _messages.MessageField('CreateCollectdTimeSeriesRequest', 1) name = _messages.StringField(2, required=True) class MonitoringProjectsGroupsCreateRequest(_messages.Message): r"""A MonitoringProjectsGroupsCreateRequest object. Fields: group: A Group resource to be passed as the request body. name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) in which to create the group. The format is: projects/[PROJECT_ID_OR_NUMBER] validateOnly: If true, validate this request but do not create the group. """ group = _messages.MessageField('Group', 1) name = _messages.StringField(2, required=True) validateOnly = _messages.BooleanField(3) class MonitoringProjectsGroupsDeleteRequest(_messages.Message): r"""A MonitoringProjectsGroupsDeleteRequest object. Fields: name: Required. The group to delete. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] recursive: If this field is true, then the request means to delete a group with all its descendants. Otherwise, the request means to delete a group only when it has no descendants. The default value is false. """ name = _messages.StringField(1, required=True) recursive = _messages.BooleanField(2) class MonitoringProjectsGroupsGetRequest(_messages.Message): r"""A MonitoringProjectsGroupsGetRequest object. Fields: name: Required. The group to retrieve. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] """ name = _messages.StringField(1, required=True) class MonitoringProjectsGroupsListRequest(_messages.Message): r"""A MonitoringProjectsGroupsListRequest object. Fields: ancestorsOfGroup: A group name. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] Returns groups that are ancestors of the specified group. The groups are returned in order, starting with the immediate parent and ending with the most distant ancestor. If the specified group has no immediate parent, the results are empty. childrenOfGroup: A group name. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] Returns groups whose parent_name field contains the group name. If no groups have this parent, the results are empty. descendantsOfGroup: A group name. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] Returns the descendants of the specified group. This is a superset of the results returned by the children_of_group filter, and includes children-of- children, and so forth. name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) whose groups are to be listed. The format is: projects/[PROJECT_ID_OR_NUMBER] pageSize: A positive number that is the maximum number of results to return. pageToken: If this field is not empty then it must contain the next_page_token value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. """ ancestorsOfGroup = _messages.StringField(1) childrenOfGroup = _messages.StringField(2) descendantsOfGroup = _messages.StringField(3) name = _messages.StringField(4, required=True) pageSize = _messages.IntegerField(5, variant=_messages.Variant.INT32) pageToken = _messages.StringField(6) class MonitoringProjectsGroupsMembersListRequest(_messages.Message): r"""A MonitoringProjectsGroupsMembersListRequest object. Fields: filter: An optional list filter (https://cloud.google.com/monitoring/api/learn_more#filtering) describing the members to be returned. The filter may reference the type, labels, and metadata of monitored resources that comprise the group. For example, to return only resources representing Compute Engine VM instances, use this filter: `resource.type = "gce_instance"` interval_endTime: Required. The end of the time interval. interval_startTime: 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. name: Required. The group whose members are listed. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] pageSize: A positive number that is the maximum number of results to return. pageToken: If this field is not empty then it must contain the next_page_token value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. """ filter = _messages.StringField(1) interval_endTime = _messages.StringField(2) interval_startTime = _messages.StringField(3) name = _messages.StringField(4, required=True) pageSize = _messages.IntegerField(5, variant=_messages.Variant.INT32) pageToken = _messages.StringField(6) class MonitoringProjectsGroupsUpdateRequest(_messages.Message): r"""A MonitoringProjectsGroupsUpdateRequest object. Fields: group: A Group resource to be passed as the request body. name: Output only. The name of this group. The format is: projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID] When creating a group, this field is ignored and a new name is created consisting of the project specified in the call to CreateGroup and a unique [GROUP_ID] that is generated automatically. validateOnly: If true, validate this request but do not update the existing group. """ group = _messages.MessageField('Group', 1) name = _messages.StringField(2, required=True) validateOnly = _messages.BooleanField(3) class MonitoringProjectsMetricDescriptorsDeleteRequest(_messages.Message): r"""A MonitoringProjectsMetricDescriptorsDeleteRequest object. Fields: name: Required. The metric descriptor on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER]/metricDescriptors/[METRIC_ID] An example of [METRIC_ID] is: "custom.googleapis.com/my_test_metric". """ name = _messages.StringField(1, required=True) class MonitoringProjectsMetricDescriptorsGetRequest(_messages.Message): r"""A MonitoringProjectsMetricDescriptorsGetRequest object. Fields: name: Required. The metric descriptor on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER]/metricDescriptors/[METRIC_ID] An example value of [METRIC_ID] is "compute.googleapis.com/instance/disk/read_bytes_count". """ name = _messages.StringField(1, required=True) class MonitoringProjectsMetricDescriptorsListRequest(_messages.Message): r"""A MonitoringProjectsMetricDescriptorsListRequest object. Fields: activeOnly: Optional. If true, only metrics and monitored resource types that have recent data (within roughly 25 hours) will be included in the response. - If a metric descriptor enumerates monitored resource types, only the monitored resource types for which the metric type has recent data will be included in the returned metric descriptor, and if none of them have recent data, the metric descriptor will not be returned. - If a metric descriptor does not enumerate the compatible monitored resource types, it will be returned only if the metric type has recent data for some monitored resource type. The returned descriptor will not enumerate any monitored resource types. filter: Optional. If this field is empty, all custom and system-defined metric descriptors are returned. Otherwise, the filter (https://cloud.google.com/monitoring/api/v3/filters) specifies which metric descriptors are to be returned. For example, the following filter matches all custom metrics (https://cloud.google.com/monitoring/custom- metrics): metric.type = starts_with("custom.googleapis.com/") name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] pageSize: Optional. A positive number that is the maximum number of results to return. The default and maximum value is 10,000. If a page_size <= 0 or > 10,000 is submitted, will instead return a maximum of 10,000 results. pageToken: Optional. If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. """ activeOnly = _messages.BooleanField(1) filter = _messages.StringField(2) name = _messages.StringField(3, required=True) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) class MonitoringProjectsMonitoredResourceDescriptorsGetRequest(_messages.Message): r"""A MonitoringProjectsMonitoredResourceDescriptorsGetRequest object. Fields: name: Required. The monitored resource descriptor to get. The format is: p rojects/[PROJECT_ID_OR_NUMBER]/monitoredResourceDescriptors/[RESOURCE_TY PE] The [RESOURCE_TYPE] is a predefined type, such as cloudsql_database. """ name = _messages.StringField(1, required=True) class MonitoringProjectsMonitoredResourceDescriptorsListRequest(_messages.Message): r"""A MonitoringProjectsMonitoredResourceDescriptorsListRequest object. Fields: filter: An optional filter (https://cloud.google.com/monitoring/api/v3/filters) describing the descriptors to be returned. The filter can reference the descriptor's type and labels. For example, the following filter returns only Google Compute Engine descriptors that have an id label: resource.type = starts_with("gce_") AND resource.label:id name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] pageSize: A positive number that is the maximum number of results to return. pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. """ filter = _messages.StringField(1) name = _messages.StringField(2, required=True) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) class MonitoringProjectsNotificationChannelDescriptorsGetRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelDescriptorsGetRequest object. Fields: name: Required. The channel type for which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannelDescriptor s/[CHANNEL_TYPE] """ name = _messages.StringField(1, required=True) class MonitoringProjectsNotificationChannelDescriptorsListRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelDescriptorsListRequest object. Fields: name: Required. The REST resource name of the parent from which to retrieve the notification channel descriptors. The expected syntax is: projects/[PROJECT_ID_OR_NUMBER] Note that this names (https://cloud.google.com/monitoring/api/v3#project_name) the parent container in which to look for the descriptors; to retrieve a single descriptor by name, use the GetNotificationChannelDescriptor operation, instead. pageSize: The maximum number of results to return in a single response. If not set to a positive number, a reasonable value will be chosen by the service. pageToken: If non-empty, page_token must contain a value returned as the next_page_token in a previous response to request the next set of results. """ name = _messages.StringField(1, required=True) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) class MonitoringProjectsNotificationChannelsCreateRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelsCreateRequest object. Fields: name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] This names the container into which the channel will be written, this does not name the newly created channel. The resulting channel's name will have a normalized version of this field as a prefix, but will add /notificationChannels/[CHANNEL_ID] to identify the channel. notificationChannel: A NotificationChannel resource to be passed as the request body. """ name = _messages.StringField(1, required=True) notificationChannel = _messages.MessageField('NotificationChannel', 2) class MonitoringProjectsNotificationChannelsDeleteRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelsDeleteRequest object. Fields: force: If true, the notification channel will be deleted regardless of its use in alert policies (the policies will be updated to remove the channel). If false, this operation will fail if the notification channel is referenced by existing alerting policies. name: Required. The channel for which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID] """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class MonitoringProjectsNotificationChannelsGetRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelsGetRequest object. Fields: name: Required. The channel for which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID] """ name = _messages.StringField(1, required=True) class MonitoringProjectsNotificationChannelsGetVerificationCodeRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelsGetVerificationCodeRequest object. Fields: getNotificationChannelVerificationCodeRequest: A GetNotificationChannelVerificationCodeRequest resource to be passed as the request body. name: Required. The notification channel for which a verification code is to be generated and retrieved. This must name a channel that is already verified; if the specified channel is not verified, the request will fail. """ getNotificationChannelVerificationCodeRequest = _messages.MessageField('GetNotificationChannelVerificationCodeRequest', 1) name = _messages.StringField(2, required=True) class MonitoringProjectsNotificationChannelsListRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelsListRequest object. Fields: filter: Optional. If provided, this field specifies the criteria that must be met by notification channels to be included in the response.For more details, see sorting and filtering (https://cloud.google.com/monitoring/api/v3/sorting-and-filtering). name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] This names the container in which to look for the notification channels; it does not name a specific channel. To query a specific channel by REST resource name, use the GetNotificationChannel operation. orderBy: Optional. A comma-separated list of fields by which to sort the result. Supports the same set of fields as in filter. Entries can be prefixed with a minus sign to sort in descending rather than ascending order.For more details, see sorting and filtering (https://cloud.google.com/monitoring/api/v3/sorting-and-filtering). pageSize: Optional. The maximum number of results to return in a single response. If not set to a positive number, a reasonable value will be chosen by the service. pageToken: Optional. If non-empty, page_token must contain a value returned as the next_page_token in a previous response to request the next set of results. """ filter = _messages.StringField(1) name = _messages.StringField(2, required=True) orderBy = _messages.StringField(3) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) class MonitoringProjectsNotificationChannelsPatchRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelsPatchRequest object. Fields: name: Identifier. The full REST resource name for this channel. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID] The [CHANNEL_ID] is automatically assigned by the server on creation. notificationChannel: A NotificationChannel resource to be passed as the request body. updateMask: Optional. The fields to update. """ name = _messages.StringField(1, required=True) notificationChannel = _messages.MessageField('NotificationChannel', 2) updateMask = _messages.StringField(3) class MonitoringProjectsNotificationChannelsSendVerificationCodeRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelsSendVerificationCodeRequest object. Fields: name: Required. The notification channel to which to send a verification code. sendNotificationChannelVerificationCodeRequest: A SendNotificationChannelVerificationCodeRequest resource to be passed as the request body. """ name = _messages.StringField(1, required=True) sendNotificationChannelVerificationCodeRequest = _messages.MessageField('SendNotificationChannelVerificationCodeRequest', 2) class MonitoringProjectsNotificationChannelsVerifyRequest(_messages.Message): r"""A MonitoringProjectsNotificationChannelsVerifyRequest object. Fields: name: Required. The notification channel to verify. verifyNotificationChannelRequest: A VerifyNotificationChannelRequest resource to be passed as the request body. """ name = _messages.StringField(1, required=True) verifyNotificationChannelRequest = _messages.MessageField('VerifyNotificationChannelRequest', 2) class MonitoringProjectsSnoozesCreateRequest(_messages.Message): r"""A MonitoringProjectsSnoozesCreateRequest object. Fields: parent: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) in which a Snooze should be created. The format is: projects/[PROJECT_ID_OR_NUMBER] snooze: A Snooze resource to be passed as the request body. """ parent = _messages.StringField(1, required=True) snooze = _messages.MessageField('Snooze', 2) class MonitoringProjectsSnoozesGetRequest(_messages.Message): r"""A MonitoringProjectsSnoozesGetRequest object. Fields: name: Required. The ID of the Snooze to retrieve. The format is: projects/[PROJECT_ID_OR_NUMBER]/snoozes/[SNOOZE_ID] """ name = _messages.StringField(1, required=True) class MonitoringProjectsSnoozesListRequest(_messages.Message): r"""A MonitoringProjectsSnoozesListRequest object. Fields: filter: Optional. Optional filter to restrict results to the given criteria. The following fields are supported. interval.start_time interval.end_timeFor example: interval.start_time > "2022-03-11T00:00:00-08:00" AND interval.end_time < "2022-03-12T00:00:00-08:00" pageSize: Optional. The maximum number of results to return for a single query. The server may further constrain the maximum number of results returned in a single page. The value should be in the range 1, 1000. If the value given is outside this range, the server will decide the number of results to be returned. pageToken: Optional. The next_page_token from a previous call to ListSnoozesRequest to get the next page of results. parent: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) whose Snoozes should be listed. The format is: projects/[PROJECT_ID_OR_NUMBER] """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) class MonitoringProjectsSnoozesPatchRequest(_messages.Message): r"""A MonitoringProjectsSnoozesPatchRequest object. Fields: name: Required. Identifier. The name of the Snooze. The format is: projects/[PROJECT_ID_OR_NUMBER]/snoozes/[SNOOZE_ID] The ID of the Snooze will be generated by the system. snooze: A Snooze resource to be passed as the request body. updateMask: Required. The fields to update.For each field listed in update_mask: If the Snooze object supplied in the UpdateSnoozeRequest has a value for that field, the value of the field in the existing Snooze will be set to the value of the field in the supplied Snooze. If the field does not have a value in the supplied Snooze, the field in the existing Snooze is set to its default value.Fields not listed retain their existing value.The following are the field names that are accepted in update_mask: display_name interval.start_time interval.end_timeThat said, the start time and end time of the Snooze determines which fields can legally be updated. Before attempting an update, users should consult the documentation for UpdateSnoozeRequest, which talks about which fields can be updated. """ name = _messages.StringField(1, required=True) snooze = _messages.MessageField('Snooze', 2) updateMask = _messages.StringField(3) class MonitoringProjectsTimeSeriesCreateRequest(_messages.Message): r"""A MonitoringProjectsTimeSeriesCreateRequest object. Fields: createTimeSeriesRequest: A CreateTimeSeriesRequest resource to be passed as the request body. name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] """ createTimeSeriesRequest = _messages.MessageField('CreateTimeSeriesRequest', 1) name = _messages.StringField(2, required=True) class MonitoringProjectsTimeSeriesCreateServiceRequest(_messages.Message): r"""A MonitoringProjectsTimeSeriesCreateServiceRequest object. Fields: createTimeSeriesRequest: A CreateTimeSeriesRequest resource to be passed as the request body. name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] """ createTimeSeriesRequest = _messages.MessageField('CreateTimeSeriesRequest', 1) name = _messages.StringField(2, required=True) class MonitoringProjectsTimeSeriesListRequest(_messages.Message): r"""A MonitoringProjectsTimeSeriesListRequest object. Enums: AggregationCrossSeriesReducerValueValuesEnum: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. AggregationPerSeriesAlignerValueValuesEnum: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. SecondaryAggregationCrossSeriesReducerValueValuesEnum: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. SecondaryAggregationPerSeriesAlignerValueValuesEnum: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. ViewValueValuesEnum: Required. Specifies which information is returned about the time series. Fields: aggregation_alignmentPeriod: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per- series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies. aggregation_crossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. aggregation_groupByFields: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored. aggregation_perSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. filter: Required. A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that specifies which time series should be returned. The filter must specify a single metric type, and can additionally specify metric labels and other information. For example: metric.type = "compute.googleapis.com/instance/cpu/usage_time" AND metric.labels.instance_name = "my-instance-name" interval_endTime: Required. The end of the time interval. interval_startTime: 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. name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name), organization or folder on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] organizations/[ORGANIZATION_ID] folders/[FOLDER_ID] orderBy: Unsupported: must be left blank. The points in each time series are currently returned in reverse time order (most recent to oldest). pageSize: A positive number that is the maximum number of results to return. If page_size is empty or more than 100,000 results, the effective page_size is 100,000 results. If view is set to FULL, this is the maximum number of Points returned. If view is set to HEADERS, this is the maximum number of TimeSeries returned. pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. secondaryAggregation_alignmentPeriod: The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.The maximum value of the alignment_period is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies. secondaryAggregation_crossSeriesReducer: The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. secondaryAggregation_groupByFields: The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored. secondaryAggregation_perSeriesAligner: An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. view: Required. Specifies which information is returned about the time series. """ class AggregationCrossSeriesReducerValueValuesEnum(_messages.Enum): r"""The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Values: REDUCE_NONE: No cross-time series reduction. The output of the Aligner is returned. REDUCE_MEAN: Reduce by computing the mean value 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. REDUCE_MIN: Reduce by computing the minimum value 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. REDUCE_MAX: Reduce by computing the maximum value 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. REDUCE_SUM: 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. REDUCE_STDDEV: 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. REDUCE_COUNT: Reduce by computing the number 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. REDUCE_COUNT_TRUE: Reduce by computing the number 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. REDUCE_COUNT_FALSE: Reduce by computing the number 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. REDUCE_FRACTION_TRUE: Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_95: Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_50: Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_05: Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/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. """ 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 = 8 REDUCE_FRACTION_TRUE = 9 REDUCE_PERCENTILE_99 = 10 REDUCE_PERCENTILE_95 = 11 REDUCE_PERCENTILE_50 = 12 REDUCE_PERCENTILE_05 = 13 class AggregationPerSeriesAlignerValueValuesEnum(_messages.Enum): r"""An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Values: ALIGN_NONE: No alignment. Raw data is returned. Not valid if cross- series reduction is requested. The value_type of the result is the same as the value_type of the input. ALIGN_DELTA: Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input. ALIGN_RATE: Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead. ALIGN_INTERPOLATE: Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_NEXT_OLDER: Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MIN: Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MAX: Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MEAN: Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE. ALIGN_COUNT: Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64. ALIGN_SUM: Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_STDDEV: Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE. ALIGN_COUNT_TRUE: Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_COUNT_FALSE: Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_FRACTION_TRUE: Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. ALIGN_PERCENTILE_99: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_95: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_50: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_05: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENT_CHANGE: Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. 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. """ ALIGN_NONE = 0 ALIGN_DELTA = 1 ALIGN_RATE = 2 ALIGN_INTERPOLATE = 3 ALIGN_NEXT_OLDER = 4 ALIGN_MIN = 5 ALIGN_MAX = 6 ALIGN_MEAN = 7 ALIGN_COUNT = 8 ALIGN_SUM = 9 ALIGN_STDDEV = 10 ALIGN_COUNT_TRUE = 11 ALIGN_COUNT_FALSE = 12 ALIGN_FRACTION_TRUE = 13 ALIGN_PERCENTILE_99 = 14 ALIGN_PERCENTILE_95 = 15 ALIGN_PERCENTILE_50 = 16 ALIGN_PERCENTILE_05 = 17 ALIGN_PERCENT_CHANGE = 18 class SecondaryAggregationCrossSeriesReducerValueValuesEnum(_messages.Enum): r"""The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Values: REDUCE_NONE: No cross-time series reduction. The output of the Aligner is returned. REDUCE_MEAN: Reduce by computing the mean value 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. REDUCE_MIN: Reduce by computing the minimum value 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. REDUCE_MAX: Reduce by computing the maximum value 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. REDUCE_SUM: 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. REDUCE_STDDEV: 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. REDUCE_COUNT: Reduce by computing the number 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. REDUCE_COUNT_TRUE: Reduce by computing the number 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. REDUCE_COUNT_FALSE: Reduce by computing the number 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. REDUCE_FRACTION_TRUE: Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_95: Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_50: Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/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. REDUCE_PERCENTILE_05: Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/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. """ 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 = 8 REDUCE_FRACTION_TRUE = 9 REDUCE_PERCENTILE_99 = 10 REDUCE_PERCENTILE_95 = 11 REDUCE_PERCENTILE_50 = 12 REDUCE_PERCENTILE_05 = 13 class SecondaryAggregationPerSeriesAlignerValueValuesEnum(_messages.Enum): r"""An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Values: ALIGN_NONE: No alignment. Raw data is returned. Not valid if cross- series reduction is requested. The value_type of the result is the same as the value_type of the input. ALIGN_DELTA: Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input. ALIGN_RATE: Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead. ALIGN_INTERPOLATE: Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_NEXT_OLDER: Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MIN: Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MAX: Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_MEAN: Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE. ALIGN_COUNT: Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64. ALIGN_SUM: Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input. ALIGN_STDDEV: Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE. ALIGN_COUNT_TRUE: Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_COUNT_FALSE: Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64. ALIGN_FRACTION_TRUE: Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE. ALIGN_PERCENTILE_99: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_95: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_50: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENTILE_05: Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE. ALIGN_PERCENT_CHANGE: Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. 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. """ ALIGN_NONE = 0 ALIGN_DELTA = 1 ALIGN_RATE = 2 ALIGN_INTERPOLATE = 3 ALIGN_NEXT_OLDER = 4 ALIGN_MIN = 5 ALIGN_MAX = 6 ALIGN_MEAN = 7 ALIGN_COUNT = 8 ALIGN_SUM = 9 ALIGN_STDDEV = 10 ALIGN_COUNT_TRUE = 11 ALIGN_COUNT_FALSE = 12 ALIGN_FRACTION_TRUE = 13 ALIGN_PERCENTILE_99 = 14 ALIGN_PERCENTILE_95 = 15 ALIGN_PERCENTILE_50 = 16 ALIGN_PERCENTILE_05 = 17 ALIGN_PERCENT_CHANGE = 18 class ViewValueValuesEnum(_messages.Enum): r"""Required. Specifies which information is returned about the time series. Values: FULL: Returns the identity of the metric(s), the time series, and the time series data. HEADERS: Returns the identity of the metric and the time series resource, but not the time series data. """ FULL = 0 HEADERS = 1 aggregation_alignmentPeriod = _messages.StringField(1) aggregation_crossSeriesReducer = _messages.EnumField('AggregationCrossSeriesReducerValueValuesEnum', 2) aggregation_groupByFields = _messages.StringField(3, repeated=True) aggregation_perSeriesAligner = _messages.EnumField('AggregationPerSeriesAlignerValueValuesEnum', 4) filter = _messages.StringField(5) interval_endTime = _messages.StringField(6) interval_startTime = _messages.StringField(7) name = _messages.StringField(8, required=True) orderBy = _messages.StringField(9) pageSize = _messages.IntegerField(10, variant=_messages.Variant.INT32) pageToken = _messages.StringField(11) secondaryAggregation_alignmentPeriod = _messages.StringField(12) secondaryAggregation_crossSeriesReducer = _messages.EnumField('SecondaryAggregationCrossSeriesReducerValueValuesEnum', 13) secondaryAggregation_groupByFields = _messages.StringField(14, repeated=True) secondaryAggregation_perSeriesAligner = _messages.EnumField('SecondaryAggregationPerSeriesAlignerValueValuesEnum', 15) view = _messages.EnumField('ViewValueValuesEnum', 16) class MonitoringProjectsTimeSeriesQueryRequest(_messages.Message): r"""A MonitoringProjectsTimeSeriesQueryRequest object. Fields: name: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] queryTimeSeriesRequest: A QueryTimeSeriesRequest resource to be passed as the request body. """ name = _messages.StringField(1, required=True) queryTimeSeriesRequest = _messages.MessageField('QueryTimeSeriesRequest', 2) class MonitoringProjectsUptimeCheckConfigsCreateRequest(_messages.Message): r"""A MonitoringProjectsUptimeCheckConfigsCreateRequest object. Fields: parent: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) in which to create the Uptime check. The format is: projects/[PROJECT_ID_OR_NUMBER] uptimeCheckConfig: A UptimeCheckConfig resource to be passed as the request body. """ parent = _messages.StringField(1, required=True) uptimeCheckConfig = _messages.MessageField('UptimeCheckConfig', 2) class MonitoringProjectsUptimeCheckConfigsDeleteRequest(_messages.Message): r"""A MonitoringProjectsUptimeCheckConfigsDeleteRequest object. Fields: name: Required. The Uptime check configuration to delete. The format is: projects/[PROJECT_ID_OR_NUMBER]/uptimeCheckConfigs/[UPTIME_CHECK_ID] """ name = _messages.StringField(1, required=True) class MonitoringProjectsUptimeCheckConfigsGetRequest(_messages.Message): r"""A MonitoringProjectsUptimeCheckConfigsGetRequest object. Fields: name: Required. The Uptime check configuration to retrieve. The format is: projects/[PROJECT_ID_OR_NUMBER]/uptimeCheckConfigs/[UPTIME_CHECK_ID] """ name = _messages.StringField(1, required=True) class MonitoringProjectsUptimeCheckConfigsListRequest(_messages.Message): r"""A MonitoringProjectsUptimeCheckConfigsListRequest object. Fields: filter: If provided, this field specifies the criteria that must be met by uptime checks to be included in the response.For more details, see Filtering syntax (https://cloud.google.com/monitoring/api/v3/sorting- and-filtering#filter_syntax). pageSize: The maximum number of results to return in a single response. The server may further constrain the maximum number of results returned in a single page. If the page_size is <=0, the server will decide the number of results to be returned. pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return more results from the previous method call. parent: Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) whose Uptime check configurations are listed. The format is: projects/[PROJECT_ID_OR_NUMBER] """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) class MonitoringProjectsUptimeCheckConfigsPatchRequest(_messages.Message): r"""A MonitoringProjectsUptimeCheckConfigsPatchRequest object. Fields: name: Identifier. A unique resource name for this Uptime check configuration. The format is: projects/[PROJECT_ID_OR_NUMBER]/uptimeCheckConfigs/[UPTIME_CHECK_ID] [PROJECT_ID_OR_NUMBER] is the Workspace host project associated with the Uptime check.This field should be omitted when creating the Uptime check configuration; on create, the resource name is assigned by the server and included in the response. updateMask: Optional. If present, only the listed fields in the current Uptime check configuration are updated with values from the new configuration. If this field is empty, then the current configuration is completely replaced with the new configuration. uptimeCheckConfig: A UptimeCheckConfig resource to be passed as the request body. """ name = _messages.StringField(1, required=True) updateMask = _messages.StringField(2) uptimeCheckConfig = _messages.MessageField('UptimeCheckConfig', 3) class MonitoringQueryLanguageCondition(_messages.Message): r"""A condition type that allows alerting policies to be defined using Monitoring Query Language (https://cloud.google.com/monitoring/mql). Enums: EvaluationMissingDataValueValuesEnum: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. Fields: duration: Optional. The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly. The default value is zero. evaluationMissingData: A condition control that determines how metric- threshold conditions are evaluated when data stops arriving. query: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream. trigger: The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by filter and aggregations, or by the ratio, if denominator_filter and denominator_aggregations are specified. """ class EvaluationMissingDataValueValuesEnum(_messages.Enum): r"""A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. Values: EVALUATION_MISSING_DATA_UNSPECIFIED: An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP. EVALUATION_MISSING_DATA_INACTIVE: If there is no data to evaluate the condition, then evaluate the condition as false. EVALUATION_MISSING_DATA_ACTIVE: If there is no data to evaluate the condition, then evaluate the condition as true. EVALUATION_MISSING_DATA_NO_OP: Do not evaluate the condition to any value if there is no data. """ EVALUATION_MISSING_DATA_UNSPECIFIED = 0 EVALUATION_MISSING_DATA_INACTIVE = 1 EVALUATION_MISSING_DATA_ACTIVE = 2 EVALUATION_MISSING_DATA_NO_OP = 3 duration = _messages.StringField(1) evaluationMissingData = _messages.EnumField('EvaluationMissingDataValueValuesEnum', 2) query = _messages.StringField(3) trigger = _messages.MessageField('Trigger', 4) class MonitoringServicesCreateRequest(_messages.Message): r"""A MonitoringServicesCreateRequest object. Fields: parent: Required. Resource name (https://cloud.google.com/monitoring/api/v3#project_name) of the parent Metrics Scope. The format is: projects/[PROJECT_ID_OR_NUMBER] service: A Service resource to be passed as the request body. serviceId: Optional. The Service id to use for this Service. If omitted, an id will be generated instead. Must match the pattern [a-z0-9\-]+ """ parent = _messages.StringField(1, required=True) service = _messages.MessageField('Service', 2) serviceId = _messages.StringField(3) class MonitoringServicesDeleteRequest(_messages.Message): r"""A MonitoringServicesDeleteRequest object. Fields: name: Required. Resource name of the Service to delete. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID] """ name = _messages.StringField(1, required=True) class MonitoringServicesGetRequest(_messages.Message): r"""A MonitoringServicesGetRequest object. Fields: name: Required. Resource name of the Service. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID] """ name = _messages.StringField(1, required=True) class MonitoringServicesListRequest(_messages.Message): r"""A MonitoringServicesListRequest object. Fields: filter: A filter specifying what Services to return. The filter supports filtering on a particular service-identifier type or one of its attributes.To filter on a particular service-identifier type, the identifier_case refers to which option in the identifier field is populated. For example, the filter identifier_case = "CUSTOM" would match all services with a value for the custom field. Valid options include "CUSTOM", "APP_ENGINE", "MESH_ISTIO", and the other options listed at https://cloud.google.com/monitoring/api/ref_v3/rest/v3/service s#ServiceTo filter on an attribute of a service-identifier type, apply the filter name by using the snake case of the service-identifier type and the attribute of that service-identifier type, and join the two with a period. For example, to filter by the meshUid field of the MeshIstio service-identifier type, you must filter on mesh_istio.mesh_uid = "123" to match all services with mesh UID "123". Service-identifier types and their attributes are described at https://cloud.google.com/monitoring/api/ref_v3/rest/v3/services#Service pageSize: A non-negative number that is the maximum number of results to return. When 0, use default page size. pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. parent: Required. Resource name of the parent containing the listed services, either a project (https://cloud.google.com/monitoring/api/v3#project_name) or a Monitoring Metrics Scope. The formats are: projects/[PROJECT_ID_OR_NUMBER] workspaces/[HOST_PROJECT_ID_OR_NUMBER] """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) class MonitoringServicesPatchRequest(_messages.Message): r"""A MonitoringServicesPatchRequest object. Fields: name: Identifier. Resource name for this Service. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID] service: A Service resource to be passed as the request body. updateMask: A set of field paths defining which fields to use for the update. """ name = _messages.StringField(1, required=True) service = _messages.MessageField('Service', 2) updateMask = _messages.StringField(3) class MonitoringServicesServiceLevelObjectivesCreateRequest(_messages.Message): r"""A MonitoringServicesServiceLevelObjectivesCreateRequest object. Fields: parent: Required. Resource name of the parent Service. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID] serviceLevelObjective: A ServiceLevelObjective resource to be passed as the request body. serviceLevelObjectiveId: Optional. The ServiceLevelObjective id to use for this ServiceLevelObjective. If omitted, an id will be generated instead. Must match the pattern ^[a-zA-Z0-9-_:.]+$ """ parent = _messages.StringField(1, required=True) serviceLevelObjective = _messages.MessageField('ServiceLevelObjective', 2) serviceLevelObjectiveId = _messages.StringField(3) class MonitoringServicesServiceLevelObjectivesDeleteRequest(_messages.Message): r"""A MonitoringServicesServiceLevelObjectivesDeleteRequest object. Fields: name: Required. Resource name of the ServiceLevelObjective to delete. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID]/service LevelObjectives/[SLO_NAME] """ name = _messages.StringField(1, required=True) class MonitoringServicesServiceLevelObjectivesGetRequest(_messages.Message): r"""A MonitoringServicesServiceLevelObjectivesGetRequest object. Enums: ViewValueValuesEnum: View of the ServiceLevelObjective to return. If DEFAULT, return the ServiceLevelObjective as originally defined. If EXPLICIT and the ServiceLevelObjective is defined in terms of a BasicSli, replace the BasicSli with a RequestBasedSli spelling out how the SLI is computed. Fields: name: Required. Resource name of the ServiceLevelObjective to get. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID]/service LevelObjectives/[SLO_NAME] view: View of the ServiceLevelObjective to return. If DEFAULT, return the ServiceLevelObjective as originally defined. If EXPLICIT and the ServiceLevelObjective is defined in terms of a BasicSli, replace the BasicSli with a RequestBasedSli spelling out how the SLI is computed. """ class ViewValueValuesEnum(_messages.Enum): r"""View of the ServiceLevelObjective to return. If DEFAULT, return the ServiceLevelObjective as originally defined. If EXPLICIT and the ServiceLevelObjective is defined in terms of a BasicSli, replace the BasicSli with a RequestBasedSli spelling out how the SLI is computed. Values: VIEW_UNSPECIFIED: Same as FULL. FULL: Return the embedded ServiceLevelIndicator in the form in which it was defined. If it was defined using a BasicSli, return that BasicSli. EXPLICIT: For ServiceLevelIndicators using BasicSli articulation, instead return the ServiceLevelIndicator with its mode of computation fully spelled out as a RequestBasedSli. For ServiceLevelIndicators using RequestBasedSli or WindowsBasedSli, return the ServiceLevelIndicator as it was provided. """ VIEW_UNSPECIFIED = 0 FULL = 1 EXPLICIT = 2 name = _messages.StringField(1, required=True) view = _messages.EnumField('ViewValueValuesEnum', 2) class MonitoringServicesServiceLevelObjectivesListRequest(_messages.Message): r"""A MonitoringServicesServiceLevelObjectivesListRequest object. Enums: ViewValueValuesEnum: View of the ServiceLevelObjectives to return. If DEFAULT, return each ServiceLevelObjective as originally defined. If EXPLICIT and the ServiceLevelObjective is defined in terms of a BasicSli, replace the BasicSli with a RequestBasedSli spelling out how the SLI is computed. Fields: filter: A filter specifying what ServiceLevelObjectives to return. pageSize: A non-negative number that is the maximum number of results to return. When 0, use default page size. pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. parent: Required. Resource name of the parent containing the listed SLOs, either a project or a Monitoring Metrics Scope. The formats are: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID] workspaces/[HOST_PROJECT_ID_OR_NUMBER]/services/- view: View of the ServiceLevelObjectives to return. If DEFAULT, return each ServiceLevelObjective as originally defined. If EXPLICIT and the ServiceLevelObjective is defined in terms of a BasicSli, replace the BasicSli with a RequestBasedSli spelling out how the SLI is computed. """ class ViewValueValuesEnum(_messages.Enum): r"""View of the ServiceLevelObjectives to return. If DEFAULT, return each ServiceLevelObjective as originally defined. If EXPLICIT and the ServiceLevelObjective is defined in terms of a BasicSli, replace the BasicSli with a RequestBasedSli spelling out how the SLI is computed. Values: VIEW_UNSPECIFIED: Same as FULL. FULL: Return the embedded ServiceLevelIndicator in the form in which it was defined. If it was defined using a BasicSli, return that BasicSli. EXPLICIT: For ServiceLevelIndicators using BasicSli articulation, instead return the ServiceLevelIndicator with its mode of computation fully spelled out as a RequestBasedSli. For ServiceLevelIndicators using RequestBasedSli or WindowsBasedSli, return the ServiceLevelIndicator as it was provided. """ VIEW_UNSPECIFIED = 0 FULL = 1 EXPLICIT = 2 filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) view = _messages.EnumField('ViewValueValuesEnum', 5) class MonitoringServicesServiceLevelObjectivesPatchRequest(_messages.Message): r"""A MonitoringServicesServiceLevelObjectivesPatchRequest object. Fields: name: Identifier. Resource name for this ServiceLevelObjective. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID]/serviceLevelOb jectives/[SLO_NAME] serviceLevelObjective: A ServiceLevelObjective resource to be passed as the request body. updateMask: A set of field paths defining which fields to use for the update. """ name = _messages.StringField(1, required=True) serviceLevelObjective = _messages.MessageField('ServiceLevelObjective', 2) updateMask = _messages.StringField(3) class MonitoringUptimeCheckIpsListRequest(_messages.Message): r"""A MonitoringUptimeCheckIpsListRequest object. Fields: pageSize: The maximum number of results to return in a single response. The server may further constrain the maximum number of results returned in a single page. If the page_size is <=0, the server will decide the number of results to be returned. NOTE: this field is not yet implemented pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return more results from the previous method call. NOTE: this field is not yet implemented """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) class MutationRecord(_messages.Message): r"""Describes a change made to a configuration. Fields: mutateTime: When the change occurred. mutatedBy: The email address of the user making the change. """ mutateTime = _messages.StringField(1) mutatedBy = _messages.StringField(2) class NotificationChannel(_messages.Message): r"""A NotificationChannel is a medium through which an alert is delivered when a policy violation is detected. Examples of channels include email, SMS, and third-party messaging applications. Fields containing sensitive information like authentication tokens or contact info are only partially populated on retrieval. Enums: VerificationStatusValueValuesEnum: Indicates whether this channel has been verified or not. On a ListNotificationChannels or GetNotificationChannel operation, this field is expected to be populated.If the value is UNVERIFIED, then it indicates that the channel is non-functioning (it both requires verification and lacks verification); otherwise, it is assumed that the channel works.If the channel is neither VERIFIED nor UNVERIFIED, it implies that the channel is of a type that does not require verification or that this specific channel has been exempted from verification because it was created prior to verification being required for channels of this type.This field cannot be modified using a standard UpdateNotificationChannel operation. To change the value of this field, you must call VerifyNotificationChannel. Messages: LabelsValue: Configuration fields that define the channel and its behavior. The permissible and required labels are specified in the NotificationChannelDescriptor.labels of the NotificationChannelDescriptor corresponding to the type field. UserLabelsValue: User-supplied key/value data that does not need to conform to the corresponding NotificationChannelDescriptor's schema, unlike the labels field. This field is intended to be used for organizing and identifying the NotificationChannel objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter. Fields: creationRecord: Record of the creation of this channel. description: An optional human-readable description of this notification channel. This description may provide additional details, beyond the display name, for the channel. This may not exceed 1024 Unicode characters. displayName: An optional human-readable name for this notification channel. It is recommended that you specify a non-empty and unique name in order to make it easier to identify the channels in your project, though this is not enforced. The display name is limited to 512 Unicode characters. enabled: Whether notifications are forwarded to the described channel. This makes it possible to disable delivery of notifications to a particular channel without removing the channel from all alerting policies that reference the channel. This is a more convenient approach when the change is temporary and you want to receive notifications from the same set of alerting policies on the channel at some point in the future. labels: Configuration fields that define the channel and its behavior. The permissible and required labels are specified in the NotificationChannelDescriptor.labels of the NotificationChannelDescriptor corresponding to the type field. mutationRecords: Records of the modification of this channel. name: Identifier. The full REST resource name for this channel. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID] The [CHANNEL_ID] is automatically assigned by the server on creation. type: The type of the notification channel. This field matches the value of the NotificationChannelDescriptor.type field. userLabels: User-supplied key/value data that does not need to conform to the corresponding NotificationChannelDescriptor's schema, unlike the labels field. This field is intended to be used for organizing and identifying the NotificationChannel objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter. verificationStatus: Indicates whether this channel has been verified or not. On a ListNotificationChannels or GetNotificationChannel operation, this field is expected to be populated.If the value is UNVERIFIED, then it indicates that the channel is non-functioning (it both requires verification and lacks verification); otherwise, it is assumed that the channel works.If the channel is neither VERIFIED nor UNVERIFIED, it implies that the channel is of a type that does not require verification or that this specific channel has been exempted from verification because it was created prior to verification being required for channels of this type.This field cannot be modified using a standard UpdateNotificationChannel operation. To change the value of this field, you must call VerifyNotificationChannel. """ class VerificationStatusValueValuesEnum(_messages.Enum): r"""Indicates whether this channel has been verified or not. On a ListNotificationChannels or GetNotificationChannel operation, this field is expected to be populated.If the value is UNVERIFIED, then it indicates that the channel is non-functioning (it both requires verification and lacks verification); otherwise, it is assumed that the channel works.If the channel is neither VERIFIED nor UNVERIFIED, it implies that the channel is of a type that does not require verification or that this specific channel has been exempted from verification because it was created prior to verification being required for channels of this type.This field cannot be modified using a standard UpdateNotificationChannel operation. To change the value of this field, you must call VerifyNotificationChannel. Values: VERIFICATION_STATUS_UNSPECIFIED: Sentinel value used to indicate that the state is unknown, omitted, or is not applicable (as in the case of channels that neither support nor require verification in order to function). UNVERIFIED: The channel has yet to be verified and requires verification to function. Note that this state also applies to the case where the verification process has been initiated by sending a verification code but where the verification code has not been submitted to complete the process. VERIFIED: It has been proven that notifications can be received on this notification channel and that someone on the project has access to messages that are delivered to that channel. """ VERIFICATION_STATUS_UNSPECIFIED = 0 UNVERIFIED = 1 VERIFIED = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Configuration fields that define the channel and its behavior. The permissible and required labels are specified in the NotificationChannelDescriptor.labels of the NotificationChannelDescriptor corresponding to the type field. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class UserLabelsValue(_messages.Message): r"""User-supplied key/value data that does not need to conform to the corresponding NotificationChannelDescriptor's schema, unlike the labels field. This field is intended to be used for organizing and identifying the NotificationChannel objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter. Messages: AdditionalProperty: An additional property for a UserLabelsValue object. Fields: additionalProperties: Additional properties of type UserLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a UserLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) creationRecord = _messages.MessageField('MutationRecord', 1) description = _messages.StringField(2) displayName = _messages.StringField(3) enabled = _messages.BooleanField(4) labels = _messages.MessageField('LabelsValue', 5) mutationRecords = _messages.MessageField('MutationRecord', 6, repeated=True) name = _messages.StringField(7) type = _messages.StringField(8) userLabels = _messages.MessageField('UserLabelsValue', 9) verificationStatus = _messages.EnumField('VerificationStatusValueValuesEnum', 10) class NotificationChannelDescriptor(_messages.Message): r"""A description of a notification channel. The descriptor includes the properties of the channel and the set of labels or fields that must be specified to configure channels of a given type. Enums: LaunchStageValueValuesEnum: The product launch stage for channels of this type. SupportedTiersValueListEntryValuesEnum: Fields: description: A human-readable description of the notification channel type. The description may include a description of the properties of the channel and pointers to external documentation. displayName: A human-readable name for the notification channel type. This form of the name is suitable for a user interface. labels: The set of labels that must be defined to identify a particular channel of the corresponding type. Each label includes a description for how that field should be populated. launchStage: The product launch stage for channels of this type. name: The full REST resource name for this descriptor. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannelDescriptors/[TYPE] In the above, [TYPE] is the value of the type field. supportedTiers: The tiers that support this notification channel; the project service tier must be one of the supported_tiers. type: The type of notification channel, such as "email" and "sms". To view the full list of channels, see Channel descriptors (https://cloud.google.com/monitoring/alerts/using-channels-api#ncd). Notification channel types are globally unique. """ class LaunchStageValueValuesEnum(_messages.Enum): r"""The product launch stage for channels of this type. Values: LAUNCH_STAGE_UNSPECIFIED: Do not use this default value. UNIMPLEMENTED: The feature is not yet implemented. Users can not use it. PRELAUNCH: Prelaunch features are hidden from users and are only visible internally. EARLY_ACCESS: Early Access features are limited to a closed group of testers. To use these features, you must sign up in advance and sign a Trusted Tester agreement (which includes confidentiality provisions). These features may be unstable, changed in backward-incompatible ways, and are not guaranteed to be released. ALPHA: Alpha is a limited availability test for releases before they are cleared for widespread use. By Alpha, all significant design issues are resolved and we are in the process of verifying functionality. Alpha customers need to apply for access, agree to applicable terms, and have their projects allowlisted. Alpha releases don't have to be feature complete, no SLAs are provided, and there are no technical support obligations, but they will be far enough along that customers can actually use them in test environments or for limited-use tests -- just like they would in normal production cases. BETA: Beta is the point at which we are ready to open a release for any customer to use. There are no SLA or technical support obligations in a Beta release. Products will be complete from a feature perspective, but may have some open outstanding issues. Beta releases are suitable for limited production use cases. GA: GA features are open to all developers and are considered stable and fully qualified for production use. DEPRECATED: Deprecated features are scheduled to be shut down and removed. For more information, see the "Deprecation Policy" section of our Terms of Service (https://cloud.google.com/terms/) and the Google Cloud Platform Subject to the Deprecation Policy (https://cloud.google.com/terms/deprecation) documentation. """ LAUNCH_STAGE_UNSPECIFIED = 0 UNIMPLEMENTED = 1 PRELAUNCH = 2 EARLY_ACCESS = 3 ALPHA = 4 BETA = 5 GA = 6 DEPRECATED = 7 class SupportedTiersValueListEntryValuesEnum(_messages.Enum): r"""SupportedTiersValueListEntryValuesEnum enum type. Values: SERVICE_TIER_UNSPECIFIED: An invalid sentinel value, used to indicate that a tier has not been provided explicitly. SERVICE_TIER_BASIC: The Cloud Monitoring 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). SERVICE_TIER_PREMIUM: The Cloud Monitoring Premium tier, a higher, more expensive tier of service that provides access to all Cloud Monitoring features, lets you use Cloud Monitoring 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). """ SERVICE_TIER_UNSPECIFIED = 0 SERVICE_TIER_BASIC = 1 SERVICE_TIER_PREMIUM = 2 description = _messages.StringField(1) displayName = _messages.StringField(2) labels = _messages.MessageField('LabelDescriptor', 3, repeated=True) launchStage = _messages.EnumField('LaunchStageValueValuesEnum', 4) name = _messages.StringField(5) supportedTiers = _messages.EnumField('SupportedTiersValueListEntryValuesEnum', 6, repeated=True) type = _messages.StringField(7) class NotificationChannelStrategy(_messages.Message): r"""Control over how the notification channels in notification_channels are notified when this alert fires, on a per-channel basis. Fields: notificationChannelNames: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID] renotifyInterval: The frequency at which to send reminder notifications for open incidents. """ notificationChannelNames = _messages.StringField(1, repeated=True) renotifyInterval = _messages.StringField(2) class NotificationRateLimit(_messages.Message): r"""Control over the rate of notifications sent to this alerting policy's notification channels. Fields: period: Not more than one notification per period. """ period = _messages.StringField(1) class OperationMetadata(_messages.Message): r"""Contains metadata for longrunning operation for the edit Metrics Scope endpoints. Enums: StateValueValuesEnum: Current state of the batch operation. Fields: createTime: The time when the batch request was received. state: Current state of the batch operation. updateTime: The time when the operation result was last updated. """ class StateValueValuesEnum(_messages.Enum): r"""Current state of the batch operation. Values: STATE_UNSPECIFIED: Invalid. CREATED: Request has been received. RUNNING: Request is actively being processed. DONE: The batch processing is done. CANCELLED: The batch processing was cancelled. """ STATE_UNSPECIFIED = 0 CREATED = 1 RUNNING = 2 DONE = 3 CANCELLED = 4 createTime = _messages.StringField(1) state = _messages.EnumField('StateValueValuesEnum', 2) updateTime = _messages.StringField(3) class Option(_messages.Message): r"""A protocol buffer option, which can be attached to a message, field, enumeration, etc.New usages of this message as an alternative to FileOptions, MessageOptions, FieldOptions, EnumOptions, EnumValueOptions, ServiceOptions, or MethodOptions are strongly discouraged. Messages: ValueValue: The option's value packed in an Any message. If the value is a primitive, the corresponding wrapper type defined in google/protobuf/wrappers.proto should be used. If the value is an enum, it should be stored as an int32 value using the google.protobuf.Int32Value type. Fields: name: The option's name. For protobuf built-in options (options defined in descriptor.proto), this is the short name. For example, "map_entry". For custom options, it should be the fully-qualified name. For example, "google.api.http". value: The option's value packed in an Any message. If the value is a primitive, the corresponding wrapper type defined in google/protobuf/wrappers.proto should be used. If the value is an enum, it should be stored as an int32 value using the google.protobuf.Int32Value type. """ @encoding.MapUnrecognizedFields('additionalProperties') class ValueValue(_messages.Message): r"""The option's value packed in an Any message. If the value is a primitive, the corresponding wrapper type defined in google/protobuf/wrappers.proto should be used. If the value is an enum, it should be stored as an int32 value using the google.protobuf.Int32Value type. Messages: AdditionalProperty: An additional property for a ValueValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ValueValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) name = _messages.StringField(1) value = _messages.MessageField('ValueValue', 2) class PerformanceThreshold(_messages.Message): r"""A PerformanceThreshold is used when each window is good when that window has a sufficiently high performance. Fields: basicSliPerformance: BasicSli to evaluate to judge window quality. performance: RequestBasedSli to evaluate to judge window quality. threshold: If window performance >= threshold, the window is counted as good. """ basicSliPerformance = _messages.MessageField('BasicSli', 1) performance = _messages.MessageField('RequestBasedSli', 2) threshold = _messages.FloatField(3) class PingConfig(_messages.Message): r"""Information involved in sending ICMP pings alongside public HTTP/TCP checks. For HTTP, the pings are performed for each part of the redirect chain. Fields: pingsCount: Number of ICMP pings. A maximum of 3 ICMP pings is currently supported. """ pingsCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) class Point(_messages.Message): r"""A single data point in a time series. Fields: interval: The time interval to which the data point applies. For GAUGE metrics, the start time is optional, but if it is supplied, it must equal the end time. For DELTA metrics, the start and end time should specify a non-zero interval, with subsequent points specifying contiguous and non-overlapping intervals. For CUMULATIVE metrics, the start and end time should specify a non-zero interval, with subsequent points specifying the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points. value: The value of the data point. """ interval = _messages.MessageField('TimeInterval', 1) value = _messages.MessageField('TypedValue', 2) class PointData(_messages.Message): r"""A point's value columns and time interval. Each point has one or more point values corresponding to the entries in point_descriptors field in the TimeSeriesDescriptor associated with this object. Fields: timeInterval: The time interval associated with the point. values: The values that make up the point. """ timeInterval = _messages.MessageField('TimeInterval', 1) values = _messages.MessageField('TypedValue', 2, repeated=True) class PolicySnapshot(_messages.Message): r"""The state of the policy at the time the alert was generated. Enums: SeverityValueValuesEnum: The severity of the alert policy. Messages: UserLabelsValue: The user labels for the alert policy. Fields: displayName: The display name of the alert policy. name: The name of the alert policy resource. In the form of "projects/PROJECT_ID_OR_NUMBER/alertPolicies/ALERT_POLICY_ID". severity: The severity of the alert policy. userLabels: The user labels for the alert policy. """ class SeverityValueValuesEnum(_messages.Enum): r"""The severity of the alert policy. Values: SEVERITY_UNSPECIFIED: No severity is specified. This is the default value. CRITICAL: This is the highest severity level. Use this if the problem could cause significant damage or downtime. ERROR: This is the medium severity level. Use this if the problem could cause minor damage or downtime. WARNING: This is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future. """ SEVERITY_UNSPECIFIED = 0 CRITICAL = 1 ERROR = 2 WARNING = 3 @encoding.MapUnrecognizedFields('additionalProperties') class UserLabelsValue(_messages.Message): r"""The user labels for the alert policy. Messages: AdditionalProperty: An additional property for a UserLabelsValue object. Fields: additionalProperties: Additional properties of type UserLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a UserLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) displayName = _messages.StringField(1) name = _messages.StringField(2) severity = _messages.EnumField('SeverityValueValuesEnum', 3) userLabels = _messages.MessageField('UserLabelsValue', 4) class PrometheusQueryLanguageCondition(_messages.Message): r"""A condition type that allows alerting policies to be defined using Prometheus Query Language (PromQL) (https://prometheus.io/docs/prometheus/latest/querying/basics/).The PrometheusQueryLanguageCondition message contains information from a Prometheus alerting rule and its associated rule group.A Prometheus alerting rule is described here (https://prometheus.io/docs/prometheus/latest/configu ration/alerting_rules/). The semantics of a Prometheus alerting rule is described here (https://prometheus.io/docs/prometheus/latest/configuration/r ecording_rules/#rule).A Prometheus rule group is described here (https://pro metheus.io/docs/prometheus/latest/configuration/recording_rules/). The semantics of a Prometheus rule group is described here (https://prometheus.i o/docs/prometheus/latest/configuration/recording_rules/#rule_group).Because Cloud Alerting has no representation of a Prometheus rule group resource, we must embed the information of the parent rule group inside each of the conditions that refer to it. We must also update the contents of all Prometheus alerts in case the information of their rule group changes.The PrometheusQueryLanguageCondition protocol buffer combines the information of the corresponding rule group and alerting rule. The structure of the PrometheusQueryLanguageCondition protocol buffer does NOT mimic the structure of the Prometheus rule group and alerting rule YAML declarations. The PrometheusQueryLanguageCondition protocol buffer may change in the future to support future rule group and/or alerting rule features. There are no new such features at the present time (2023-06-26). Messages: LabelsValue: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and- labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables#doc-vars). The only available variable names are the names of the labels in the PromQL result, including "__name__" and "value". "labels" may be empty. Fields: alertRule: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric- names-and-labels). This field may not exceed 2048 Unicode characters in length. disableMetricValidation: Optional. Whether to disable metric existence validation for this condition.This allows alerting policies to be defined on metrics that do not yet exist, improving advanced customer workflows such as configuring alerting policies using Terraform.Users with the monitoring.alertPolicyViewer role are able to see the name of the non-existent metric in the alerting policy condition. duration: Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero. evaluationInterval: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group. labels: Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and- labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables#doc-vars). The only available variable names are the names of the labels in the PromQL result, including "__name__" and "value". "labels" may be empty. query: Required. The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty. ruleGroup: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). Label values can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables#doc-vars). The only available variable names are the names of the labels in the PromQL result, including "__name__" and "value". "labels" may be empty. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) alertRule = _messages.StringField(1) disableMetricValidation = _messages.BooleanField(2) duration = _messages.StringField(3) evaluationInterval = _messages.StringField(4) labels = _messages.MessageField('LabelsValue', 5) query = _messages.StringField(6) ruleGroup = _messages.StringField(7) class QueryTimeSeriesRequest(_messages.Message): r"""The QueryTimeSeries request. For information about the status of Monitoring Query Language (MQL), see the MQL deprecation notice (https://cloud.google.com/stackdriver/docs/deprecations/mql). Fields: pageSize: A positive number that is the maximum number of time_series_data to return. pageToken: If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call. query: Required. The query in the Monitoring Query Language (https://cloud.google.com/monitoring/mql/reference) format. The default time zone is in UTC. """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) query = _messages.StringField(3) class QueryTimeSeriesResponse(_messages.Message): r"""The QueryTimeSeries response. For information about the status of Monitoring Query Language (MQL), see the MQL deprecation notice (https://cloud.google.com/stackdriver/docs/deprecations/mql). Fields: nextPageToken: If there are more results than have been returned, then this field is set to a non-empty value. To see the additional results, use that value as page_token in the next call to this method. partialErrors: Query execution errors that may have caused the time series data returned to be incomplete. The available data will be available in the response. timeSeriesData: The time series data. timeSeriesDescriptor: The descriptor for the time series data. """ nextPageToken = _messages.StringField(1) partialErrors = _messages.MessageField('Status', 2, repeated=True) timeSeriesData = _messages.MessageField('TimeSeriesData', 3, repeated=True) timeSeriesDescriptor = _messages.MessageField('TimeSeriesDescriptor', 4) class Range(_messages.Message): r"""The range of the population values. Fields: max: The maximum of the population values. min: The minimum of the population values. """ max = _messages.FloatField(1) min = _messages.FloatField(2) class RequestBasedSli(_messages.Message): r"""Service Level Indicators for which atomic units of service are counted directly. Fields: distributionCut: distribution_cut is used when good_service is a count of values aggregated in a Distribution that fall into a good range. The total_service is the total count of all values aggregated in the Distribution. goodTotalRatio: good_total_ratio is used when the ratio of good_service to total_service is computed from two TimeSeries. """ distributionCut = _messages.MessageField('DistributionCut', 1) goodTotalRatio = _messages.MessageField('TimeSeriesRatio', 2) class ResourceGroup(_messages.Message): r"""The resource submessage for group checks. It can be used instead of a monitored resource, when multiple resources are being monitored. Enums: ResourceTypeValueValuesEnum: The resource type of the group members. Fields: groupId: The group of resources being monitored. Should be only the [GROUP_ID], and not the full-path projects/[PROJECT_ID_OR_NUMBER]/groups/[GROUP_ID]. resourceType: The resource type of the group members. """ class ResourceTypeValueValuesEnum(_messages.Enum): r"""The resource type of the group members. Values: RESOURCE_TYPE_UNSPECIFIED: Default value (not valid). INSTANCE: A group of instances from Google Cloud Platform (GCP) or Amazon Web Services (AWS). AWS_ELB_LOAD_BALANCER: A group of Amazon ELB load balancers. """ RESOURCE_TYPE_UNSPECIFIED = 0 INSTANCE = 1 AWS_ELB_LOAD_BALANCER = 2 groupId = _messages.StringField(1) resourceType = _messages.EnumField('ResourceTypeValueValuesEnum', 2) class ResponseStatusCode(_messages.Message): r"""A status to accept. Either a status code class like "2xx", or an integer status code like "200". Enums: StatusClassValueValuesEnum: A class of status codes to accept. Fields: statusClass: A class of status codes to accept. statusValue: A status code to accept. """ class StatusClassValueValuesEnum(_messages.Enum): r"""A class of status codes to accept. Values: STATUS_CLASS_UNSPECIFIED: Default value that matches no status codes. STATUS_CLASS_1XX: The class of status codes between 100 and 199. STATUS_CLASS_2XX: The class of status codes between 200 and 299. STATUS_CLASS_3XX: The class of status codes between 300 and 399. STATUS_CLASS_4XX: The class of status codes between 400 and 499. STATUS_CLASS_5XX: The class of status codes between 500 and 599. STATUS_CLASS_ANY: The class of all status codes. """ STATUS_CLASS_UNSPECIFIED = 0 STATUS_CLASS_1XX = 1 STATUS_CLASS_2XX = 2 STATUS_CLASS_3XX = 3 STATUS_CLASS_4XX = 4 STATUS_CLASS_5XX = 5 STATUS_CLASS_ANY = 6 statusClass = _messages.EnumField('StatusClassValueValuesEnum', 1) statusValue = _messages.IntegerField(2, variant=_messages.Variant.INT32) class RowCountTest(_messages.Message): r"""A test that checks if the number of rows in the result set violates some threshold. Enums: ComparisonValueValuesEnum: Required. The comparison to apply between the number of rows returned by the query and the threshold. Fields: comparison: Required. The comparison to apply between the number of rows returned by the query and the threshold. threshold: Required. The value against which to compare the row count. """ class ComparisonValueValuesEnum(_messages.Enum): r"""Required. The comparison to apply between the number of rows returned by the query and the threshold. Values: COMPARISON_UNSPECIFIED: No ordering relationship is specified. COMPARISON_GT: True if the left argument is greater than the right argument. COMPARISON_GE: True if the left argument is greater than or equal to the right argument. COMPARISON_LT: True if the left argument is less than the right argument. COMPARISON_LE: True if the left argument is less than or equal to the right argument. COMPARISON_EQ: True if the left argument is equal to the right argument. COMPARISON_NE: True if the left argument is not equal to the right argument. """ COMPARISON_UNSPECIFIED = 0 COMPARISON_GT = 1 COMPARISON_GE = 2 COMPARISON_LT = 3 COMPARISON_LE = 4 COMPARISON_EQ = 5 COMPARISON_NE = 6 comparison = _messages.EnumField('ComparisonValueValuesEnum', 1) threshold = _messages.IntegerField(2) class SendNotificationChannelVerificationCodeRequest(_messages.Message): r"""The SendNotificationChannelVerificationCode request.""" class Service(_messages.Message): r"""A Service is a discrete, autonomous, and network-accessible unit, designed to solve an individual concern (Wikipedia (https://en.wikipedia.org/wiki/Service-orientation)). In Cloud Monitoring, a Service acts as the root resource under which operational aspects of the service are accessible. Messages: UserLabelsValue: Labels which have been used to annotate the service. Label keys must start with a letter. Label keys and values may contain lowercase letters, numbers, underscores, and dashes. Label keys and values have a maximum length of 63 characters, and must be less than 128 bytes in size. Up to 64 label entries may be stored. For labels which do not have a semantic value, the empty string may be supplied for the label value. Fields: appEngine: Type used for App Engine services. basicService: Message that contains the service type and service labels of this service if it is a basic service. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo- monitoring/api/api-structures#basic-svc-w-basic-sli). cloudEndpoints: Type used for Cloud Endpoints services. cloudRun: Type used for Cloud Run services. clusterIstio: Type used for Istio services that live in a Kubernetes cluster. custom: Custom service type. displayName: Name used for UI elements listing this Service. gkeNamespace: Type used for GKE Namespaces. gkeService: Type used for GKE Services (the Kubernetes concept of a service). gkeWorkload: Type used for GKE Workloads. istioCanonicalService: Type used for canonical services scoped to an Istio mesh. Metrics for Istio are documented here (https://istio.io/latest/docs/reference/config/metrics/) meshIstio: Type used for Istio services scoped to an Istio mesh. name: Identifier. Resource name for this Service. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID] telemetry: Configuration for how to query telemetry on a Service. userLabels: Labels which have been used to annotate the service. Label keys must start with a letter. Label keys and values may contain lowercase letters, numbers, underscores, and dashes. Label keys and values have a maximum length of 63 characters, and must be less than 128 bytes in size. Up to 64 label entries may be stored. For labels which do not have a semantic value, the empty string may be supplied for the label value. """ @encoding.MapUnrecognizedFields('additionalProperties') class UserLabelsValue(_messages.Message): r"""Labels which have been used to annotate the service. Label keys must start with a letter. Label keys and values may contain lowercase letters, numbers, underscores, and dashes. Label keys and values have a maximum length of 63 characters, and must be less than 128 bytes in size. Up to 64 label entries may be stored. For labels which do not have a semantic value, the empty string may be supplied for the label value. Messages: AdditionalProperty: An additional property for a UserLabelsValue object. Fields: additionalProperties: Additional properties of type UserLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a UserLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) appEngine = _messages.MessageField('AppEngine', 1) basicService = _messages.MessageField('BasicService', 2) cloudEndpoints = _messages.MessageField('CloudEndpoints', 3) cloudRun = _messages.MessageField('CloudRun', 4) clusterIstio = _messages.MessageField('ClusterIstio', 5) custom = _messages.MessageField('Custom', 6) displayName = _messages.StringField(7) gkeNamespace = _messages.MessageField('GkeNamespace', 8) gkeService = _messages.MessageField('GkeService', 9) gkeWorkload = _messages.MessageField('GkeWorkload', 10) istioCanonicalService = _messages.MessageField('IstioCanonicalService', 11) meshIstio = _messages.MessageField('MeshIstio', 12) name = _messages.StringField(13) telemetry = _messages.MessageField('Telemetry', 14) userLabels = _messages.MessageField('UserLabelsValue', 15) class ServiceAgentAuthentication(_messages.Message): r"""Contains information needed for generating either an OpenID Connect token (https://developers.google.com/identity/protocols/OpenIDConnect) or OAuth token (https://developers.google.com/identity/protocols/oauth2). The token will be generated for the Monitoring service agent service account. Enums: TypeValueValuesEnum: Type of authentication. Fields: type: Type of authentication. """ class TypeValueValuesEnum(_messages.Enum): r"""Type of authentication. Values: SERVICE_AGENT_AUTHENTICATION_TYPE_UNSPECIFIED: Default value, will result in OIDC Authentication. OIDC_TOKEN: OIDC Authentication """ SERVICE_AGENT_AUTHENTICATION_TYPE_UNSPECIFIED = 0 OIDC_TOKEN = 1 type = _messages.EnumField('TypeValueValuesEnum', 1) class ServiceLevelIndicator(_messages.Message): r"""A Service-Level Indicator (SLI) describes the "performance" of a service. For some services, the SLI is well-defined. In such cases, the SLI can be described easily by referencing the well-known SLI and providing the needed parameters. Alternatively, a "custom" SLI can be defined with a query to the underlying metric store. An SLI is defined to be good_service / total_service over any queried time interval. The value of performance always falls into the range 0 <= performance <= 1. A custom SLI describes how to compute this ratio, whether this is by dividing values from a pair of time series, cutting a Distribution into good and bad counts, or counting time windows in which the service complies with a criterion. For separation of concerns, a single Service-Level Indicator measures performance for only one aspect of service quality, such as fraction of successful queries or fast-enough queries. Fields: basicSli: Basic SLI on a well-known service type. requestBased: Request-based SLIs windowsBased: Windows-based SLIs """ basicSli = _messages.MessageField('BasicSli', 1) requestBased = _messages.MessageField('RequestBasedSli', 2) windowsBased = _messages.MessageField('WindowsBasedSli', 3) class ServiceLevelObjective(_messages.Message): r"""A Service-Level Objective (SLO) describes a level of desired good service. It consists of a service-level indicator (SLI), a performance goal, and a period over which the objective is to be evaluated against that goal. The SLO can use SLIs defined in a number of different manners. Typical SLOs might include "99% of requests in each rolling week have latency below 200 milliseconds" or "99.5% of requests in each calendar month return successfully." Enums: CalendarPeriodValueValuesEnum: A calendar period, semantically "since the start of the current ". At this time, only DAY, WEEK, FORTNIGHT, and MONTH are supported. Messages: UserLabelsValue: Labels which have been used to annotate the service-level objective. Label keys must start with a letter. Label keys and values may contain lowercase letters, numbers, underscores, and dashes. Label keys and values have a maximum length of 63 characters, and must be less than 128 bytes in size. Up to 64 label entries may be stored. For labels which do not have a semantic value, the empty string may be supplied for the label value. Fields: calendarPeriod: A calendar period, semantically "since the start of the current ". At this time, only DAY, WEEK, FORTNIGHT, and MONTH are supported. displayName: Name used for UI elements listing this SLO. goal: The fraction of service that must be good in order for this objective to be met. 0 < goal <= 0.9999. name: Identifier. Resource name for this ServiceLevelObjective. The format is: projects/[PROJECT_ID_OR_NUMBER]/services/[SERVICE_ID]/serviceLevelOb jectives/[SLO_NAME] rollingPeriod: A rolling time period, semantically "in the past ". Must be an integer multiple of 1 day no larger than 30 days. serviceLevelIndicator: The definition of good service, used to measure and calculate the quality of the Service's performance with respect to a single aspect of service quality. userLabels: Labels which have been used to annotate the service-level objective. Label keys must start with a letter. Label keys and values may contain lowercase letters, numbers, underscores, and dashes. Label keys and values have a maximum length of 63 characters, and must be less than 128 bytes in size. Up to 64 label entries may be stored. For labels which do not have a semantic value, the empty string may be supplied for the label value. """ class CalendarPeriodValueValuesEnum(_messages.Enum): r"""A calendar period, semantically "since the start of the current ". At this time, only DAY, WEEK, FORTNIGHT, and MONTH are supported. Values: CALENDAR_PERIOD_UNSPECIFIED: Undefined period, raises an error. DAY: A day. WEEK: A week. Weeks begin on Monday, following ISO 8601 (https://en.wikipedia.org/wiki/ISO_week_date). FORTNIGHT: A fortnight. The first calendar fortnight of the year begins at the start of week 1 according to ISO 8601 (https://en.wikipedia.org/wiki/ISO_week_date). MONTH: A month. QUARTER: A quarter. Quarters start on dates 1-Jan, 1-Apr, 1-Jul, and 1-Oct of each year. HALF: A half-year. Half-years start on dates 1-Jan and 1-Jul. YEAR: A year. """ CALENDAR_PERIOD_UNSPECIFIED = 0 DAY = 1 WEEK = 2 FORTNIGHT = 3 MONTH = 4 QUARTER = 5 HALF = 6 YEAR = 7 @encoding.MapUnrecognizedFields('additionalProperties') class UserLabelsValue(_messages.Message): r"""Labels which have been used to annotate the service-level objective. Label keys must start with a letter. Label keys and values may contain lowercase letters, numbers, underscores, and dashes. Label keys and values have a maximum length of 63 characters, and must be less than 128 bytes in size. Up to 64 label entries may be stored. For labels which do not have a semantic value, the empty string may be supplied for the label value. Messages: AdditionalProperty: An additional property for a UserLabelsValue object. Fields: additionalProperties: Additional properties of type UserLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a UserLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) calendarPeriod = _messages.EnumField('CalendarPeriodValueValuesEnum', 1) displayName = _messages.StringField(2) goal = _messages.FloatField(3) name = _messages.StringField(4) rollingPeriod = _messages.StringField(5) serviceLevelIndicator = _messages.MessageField('ServiceLevelIndicator', 6) userLabels = _messages.MessageField('UserLabelsValue', 7) class Snooze(_messages.Message): r"""A Snooze will prevent any alerts from being opened, and close any that are already open. The Snooze will work on alerts that match the criteria defined in the Snooze. The Snooze will be active from interval.start_time through interval.end_time. Fields: criteria: Required. This defines the criteria for applying the Snooze. See Criteria for more information. displayName: Required. A display name for the Snooze. This can be, at most, 512 unicode characters. interval: Required. The Snooze will be active from interval.start_time through interval.end_time. interval.start_time cannot be in the past. There is a 15 second clock skew to account for the time it takes for a request to reach the API from the UI. name: Required. Identifier. The name of the Snooze. The format is: projects/[PROJECT_ID_OR_NUMBER]/snoozes/[SNOOZE_ID] The ID of the Snooze will be generated by the system. """ criteria = _messages.MessageField('Criteria', 1) displayName = _messages.StringField(2) interval = _messages.MessageField('TimeInterval', 3) name = _messages.StringField(4) class SourceContext(_messages.Message): r"""SourceContext represents information about the source of a protobuf element, like the file in which it is defined. Fields: fileName: The path-qualified name of the .proto file that contained the associated protobuf element. For example: "google/protobuf/source_context.proto". """ fileName = _messages.StringField(1) class SpanContext(_messages.Message): r"""The context of a span. This is attached to an Exemplar in Distribution values during aggregation.It contains the name of a span with format: projects/[PROJECT_ID_OR_NUMBER]/traces/[TRACE_ID]/spans/[SPAN_ID] Fields: spanName: The resource name of the span. The format is: projects/[PROJECT_ID_OR_NUMBER]/traces/[TRACE_ID]/spans/[SPAN_ID] [TRACE_ID] is a unique identifier for a trace within a project; it is a 32-character hexadecimal encoding of a 16-byte array.[SPAN_ID] is a unique identifier for a span within a trace; it is a 16-character hexadecimal encoding of an 8-byte array. """ spanName = _messages.StringField(1) class SqlCondition(_messages.Message): r"""A condition that allows alerting policies to be defined using GoogleSQL. SQL conditions examine a sliding window of logs using GoogleSQL. Alert policies with SQL conditions may incur additional billing. Fields: booleanTest: Test the boolean value in the indicated column. daily: Schedule the query to execute every so many days. hourly: Schedule the query to execute every so many hours. minutes: Schedule the query to execute every so many minutes. query: Required. The Log Analytics SQL query to run, as a string. The query must conform to the required shape. Specifically, the query must not try to filter the input by time. A filter will automatically be applied to filter the input so that the query receives all rows received since the last time the query was run.For example, the following query extracts all log entries containing an HTTP request: SELECT timestamp, log_name, severity, http_request, resource, labels FROM my- project.global._Default._AllLogs WHERE http_request IS NOT NULL rowCountTest: Test the row count against a threshold. """ booleanTest = _messages.MessageField('BooleanTest', 1) daily = _messages.MessageField('Daily', 2) hourly = _messages.MessageField('Hourly', 3) minutes = _messages.MessageField('Minutes', 4) query = _messages.StringField(5) rowCountTest = _messages.MessageField('RowCountTest', 6) class StandardQueryParameters(_messages.Message): r"""Query parameters accepted by all methods. Enums: FXgafvValueValuesEnum: V1 error format. AltValueValuesEnum: Data format for response. Fields: f__xgafv: V1 error format. access_token: OAuth access token. alt: Data format for response. callback: JSONP fields: Selector specifying which fields to include in a partial response. key: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token. oauth_token: OAuth 2.0 token for the current user. prettyPrint: Returns response with indentations and line breaks. quotaUser: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters. trace: A tracing token of the form "token:" to include in api requests. uploadType: Legacy upload protocol for media (e.g. "media", "multipart"). upload_protocol: Upload protocol for media (e.g. "raw", "multipart"). """ class AltValueValuesEnum(_messages.Enum): r"""Data format for response. Values: json: Responses with Content-Type of application/json media: Media download with context-dependent Content-Type proto: Responses with Content-Type of application/x-protobuf """ json = 0 media = 1 proto = 2 class FXgafvValueValuesEnum(_messages.Enum): r"""V1 error format. Values: _1: v1 error format _2: v2 error format """ _1 = 0 _2 = 1 f__xgafv = _messages.EnumField('FXgafvValueValuesEnum', 1) access_token = _messages.StringField(2) alt = _messages.EnumField('AltValueValuesEnum', 3, default='json') callback = _messages.StringField(4) fields = _messages.StringField(5) key = _messages.StringField(6) oauth_token = _messages.StringField(7) prettyPrint = _messages.BooleanField(8, default=True) quotaUser = _messages.StringField(9) trace = _messages.StringField(10) uploadType = _messages.StringField(11) upload_protocol = _messages.StringField(12) class Status(_messages.Message): r"""The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). Messages: DetailsValueListEntry: A DetailsValueListEntry object. Fields: code: The status code, which should be an enum value of google.rpc.Code. details: A list of messages that carry the error details. There is a common set of message types for APIs to use. message: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. """ @encoding.MapUnrecognizedFields('additionalProperties') class DetailsValueListEntry(_messages.Message): r"""A DetailsValueListEntry object. Messages: AdditionalProperty: An additional property for a DetailsValueListEntry object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a DetailsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) code = _messages.IntegerField(1, variant=_messages.Variant.INT32) details = _messages.MessageField('DetailsValueListEntry', 2, repeated=True) message = _messages.StringField(3) class SyntheticMonitorTarget(_messages.Message): r"""Describes a Synthetic Monitor to be invoked by Uptime. Fields: cloudFunctionV2: Target a Synthetic Monitor GCFv2 instance. """ cloudFunctionV2 = _messages.MessageField('CloudFunctionV2Target', 1) class TcpCheck(_messages.Message): r"""Information required for a TCP Uptime check request. Fields: pingConfig: Contains information needed to add pings to a TCP check. port: The TCP port on the server against which to run the check. Will be combined with host (specified within the monitored_resource) to construct the full URL. Required. """ pingConfig = _messages.MessageField('PingConfig', 1) port = _messages.IntegerField(2, variant=_messages.Variant.INT32) class Telemetry(_messages.Message): r"""Configuration for how to query telemetry on a Service. Fields: resourceName: The full name of the resource that defines this service. Formatted as described in https://cloud.google.com/apis/design/resource_names. """ resourceName = _messages.StringField(1) class TimeInterval(_messages.Message): r"""Describes a time interval: Reads: A half-open time interval. It includes the end time but excludes the start time: (startTime, endTime]. The start time must be specified, must be earlier than the end time, and should be no older than the data retention period for the metric. Writes: 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 (https:/ /cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.metricDescriptors#M etricKind) of the metric value. The end time must not be earlier than the start time, and the end time must not be more than 25 hours in the past or more than five minutes in the future. For GAUGE metrics, 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. The end time of a new interval must be at least a millisecond after the end time of the previous interval. For DELTA metrics, the start time and end time must specify a non-zero interval, with subsequent points specifying contiguous and non-overlapping intervals. For DELTA metrics, the start time of the next interval must be at least a millisecond after the end time of the previous interval. For CUMULATIVE metrics, the start time and end time must specify a non-zero interval, with subsequent points specifying the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points. The new start time must be at least a millisecond after the end time of the previous interval. The start time of a new interval must be at least a millisecond after the end time of the previous interval because intervals are closed. If the start time of a new interval is the same as the end time of the previous interval, then data written at the new start time could overwrite data written at the previous end time. Fields: endTime: Required. The end of the time interval. startTime: 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. """ endTime = _messages.StringField(1) startTime = _messages.StringField(2) class TimeOfDay(_messages.Message): r"""Represents a time of day. The date and time zone are either not significant or are specified elsewhere. An API may choose to allow leap seconds. Related types are google.type.Date and google.protobuf.Timestamp. Fields: hours: Hours of a day in 24 hour format. Must be greater than or equal to 0 and typically must be less than or equal to 23. An API may choose to allow the value "24:00:00" for scenarios like business closing time. minutes: Minutes of an hour. Must be greater than or equal to 0 and less than or equal to 59. nanos: Fractions of seconds, in nanoseconds. Must be greater than or equal to 0 and less than or equal to 999,999,999. seconds: Seconds of a minute. Must be greater than or equal to 0 and typically must be less than or equal to 59. An API may allow the value 60 if it allows leap-seconds. """ hours = _messages.IntegerField(1, variant=_messages.Variant.INT32) minutes = _messages.IntegerField(2, variant=_messages.Variant.INT32) nanos = _messages.IntegerField(3, variant=_messages.Variant.INT32) seconds = _messages.IntegerField(4, variant=_messages.Variant.INT32) class TimeSeries(_messages.Message): r"""A collection of data points that describes the time-varying values of a metric. A time series is identified by a combination of a fully-specified monitored resource and a fully-specified metric. This type is used for both listing and creating time series. Enums: MetricKindValueValuesEnum: The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series.When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto- created, then this field specifies the metric kind of the new descriptor and must be either GAUGE (the default) or CUMULATIVE. ValueTypeValueValuesEnum: The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series.When creating a time series, this field is optional. If present, it must be the same as the type of the data in the points field. Fields: description: Input only. A detailed description of the time series that will be associated with the google.api.MetricDescriptor for the metric. Once set, this field cannot be changed through CreateTimeSeries. metadata: Output only. The associated monitored resource metadata. When reading a time series, this field will include metadata labels that are explicitly named in the reduction. When creating a time series, this field is ignored. metric: The associated metric. A fully-specified metric used to identify the time series. metricKind: The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series.When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto-created, then this field specifies the metric kind of the new descriptor and must be either GAUGE (the default) or CUMULATIVE. points: The data points of this time series. When listing time series, points are returned in reverse time order.When creating a time series, this field must contain exactly one point and the point's type must be the same as the value type of the associated metric. If the associated metric's descriptor must be auto-created, then the value type of the descriptor is determined by the point's type, which must be BOOL, INT64, DOUBLE, or DISTRIBUTION. resource: The associated monitored resource. Custom metrics can use only certain monitored resource types in their time series data. For more information, see Monitored resources for custom metrics (https://cloud.google.com/monitoring/custom-metrics/creating- metrics#custom-metric-resources). unit: The units in which the metric value is reported. It is only applicable if the value_type is INT64, DOUBLE, or DISTRIBUTION. The unit defines the representation of the stored metric values. This field can only be changed through CreateTimeSeries when it is empty. valueType: The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series.When creating a time series, this field is optional. If present, it must be the same as the type of the data in the points field. """ class MetricKindValueValuesEnum(_messages.Enum): r"""The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series.When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto-created, then this field specifies the metric kind of the new descriptor and must be either GAUGE (the default) or CUMULATIVE. Values: METRIC_KIND_UNSPECIFIED: Do not use this default value. GAUGE: An instantaneous measurement of a value. DELTA: The change in a value during a time interval. CUMULATIVE: A value accumulated over a time interval. Cumulative measurements in a time series should have the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points. """ METRIC_KIND_UNSPECIFIED = 0 GAUGE = 1 DELTA = 2 CUMULATIVE = 3 class ValueTypeValueValuesEnum(_messages.Enum): r"""The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series.When creating a time series, this field is optional. If present, it must be the same as the type of the data in the points field. Values: VALUE_TYPE_UNSPECIFIED: Do not use this default value. BOOL: The value is a boolean. This value type can be used only if the metric kind is GAUGE. INT64: The value is a signed 64-bit integer. DOUBLE: The value is a double precision floating point number. STRING: The value is a text string. This value type can be used only if the metric kind is GAUGE. DISTRIBUTION: The value is a Distribution. MONEY: The value is money. """ VALUE_TYPE_UNSPECIFIED = 0 BOOL = 1 INT64 = 2 DOUBLE = 3 STRING = 4 DISTRIBUTION = 5 MONEY = 6 description = _messages.StringField(1) metadata = _messages.MessageField('MonitoredResourceMetadata', 2) metric = _messages.MessageField('Metric', 3) metricKind = _messages.EnumField('MetricKindValueValuesEnum', 4) points = _messages.MessageField('Point', 5, repeated=True) resource = _messages.MessageField('MonitoredResource', 6) unit = _messages.StringField(7) valueType = _messages.EnumField('ValueTypeValueValuesEnum', 8) class TimeSeriesData(_messages.Message): r"""Represents the values of a time series associated with a TimeSeriesDescriptor. Fields: labelValues: The values of the labels in the time series identifier, given in the same order as the label_descriptors field of the TimeSeriesDescriptor associated with this object. Each value must have a value of the type given in the corresponding entry of label_descriptors. pointData: The points in the time series. """ labelValues = _messages.MessageField('LabelValue', 1, repeated=True) pointData = _messages.MessageField('PointData', 2, repeated=True) class TimeSeriesDescriptor(_messages.Message): r"""A descriptor for the labels and points in a time series. Fields: labelDescriptors: Descriptors for the labels. pointDescriptors: Descriptors for the point data value columns. """ labelDescriptors = _messages.MessageField('LabelDescriptor', 1, repeated=True) pointDescriptors = _messages.MessageField('ValueDescriptor', 2, repeated=True) class TimeSeriesRatio(_messages.Message): r"""A TimeSeriesRatio specifies two TimeSeries to use for computing the good_service / total_service ratio. The specified TimeSeries must have ValueType = DOUBLE or ValueType = INT64 and must have MetricKind = DELTA or MetricKind = CUMULATIVE. The TimeSeriesRatio must specify exactly two of good, bad, and total, and the relationship good_service + bad_service = total_service will be assumed. Fields: badServiceFilter: A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) specifying a TimeSeries quantifying bad service, either demanded service that was not provided or demanded service that was of inadequate quality. Must have ValueType = DOUBLE or ValueType = INT64 and must have MetricKind = DELTA or MetricKind = CUMULATIVE. goodServiceFilter: A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) specifying a TimeSeries quantifying good service provided. Must have ValueType = DOUBLE or ValueType = INT64 and must have MetricKind = DELTA or MetricKind = CUMULATIVE. totalServiceFilter: A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) specifying a TimeSeries quantifying total demanded service. Must have ValueType = DOUBLE or ValueType = INT64 and must have MetricKind = DELTA or MetricKind = CUMULATIVE. """ badServiceFilter = _messages.StringField(1) goodServiceFilter = _messages.StringField(2) totalServiceFilter = _messages.StringField(3) class Trigger(_messages.Message): r"""Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. Fields: count: The absolute number of time series that must fail the predicate for the condition to be triggered. percent: The percentage of time series that must fail the predicate for the condition to be triggered. """ count = _messages.IntegerField(1, variant=_messages.Variant.INT32) percent = _messages.FloatField(2) class Type(_messages.Message): r"""A protocol buffer message type.New usages of this message as an alternative to DescriptorProto are strongly discouraged. This message does not reliability preserve all information necessary to model the schema and preserve semantics. Instead make use of FileDescriptorSet which preserves the necessary information. Enums: SyntaxValueValuesEnum: The source syntax. Fields: edition: The source edition string, only valid when syntax is SYNTAX_EDITIONS. fields: The list of fields. name: The fully qualified message name. oneofs: The list of types appearing in oneof definitions in this type. options: The protocol buffer options. sourceContext: The source context. syntax: The source syntax. """ class SyntaxValueValuesEnum(_messages.Enum): r"""The source syntax. Values: SYNTAX_PROTO2: Syntax proto2. SYNTAX_PROTO3: Syntax proto3. SYNTAX_EDITIONS: Syntax editions. """ SYNTAX_PROTO2 = 0 SYNTAX_PROTO3 = 1 SYNTAX_EDITIONS = 2 edition = _messages.StringField(1) fields = _messages.MessageField('Field', 2, repeated=True) name = _messages.StringField(3) oneofs = _messages.StringField(4, repeated=True) options = _messages.MessageField('Option', 5, repeated=True) sourceContext = _messages.MessageField('SourceContext', 6) syntax = _messages.EnumField('SyntaxValueValuesEnum', 7) class TypedValue(_messages.Message): r"""A single strongly-typed value. Fields: boolValue: A Boolean value: true or false. distributionValue: A distribution value. doubleValue: A 64-bit double-precision floating-point number. Its magnitude is approximately \xb110\xb1300 and it has 16 significant digits of precision. int64Value: A 64-bit integer. Its range is approximately \xb19.2x1018. stringValue: A variable-length string value. """ boolValue = _messages.BooleanField(1) distributionValue = _messages.MessageField('Distribution', 2) doubleValue = _messages.FloatField(3) int64Value = _messages.IntegerField(4) stringValue = _messages.StringField(5) class UptimeCheckConfig(_messages.Message): r"""This message configures which resources and services to monitor for availability. Enums: CheckerTypeValueValuesEnum: The type of checkers to use to execute the Uptime check. SelectedRegionsValueListEntryValuesEnum: Messages: UserLabelsValue: User-supplied key/value data to be used for organizing and identifying the UptimeCheckConfig objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter. Fields: checkerType: The type of checkers to use to execute the Uptime check. contentMatchers: The content that is expected to appear in the data returned by the target server against which the check is run. Currently, only the first entry in the content_matchers list is supported, and additional entries will be ignored. This field is optional and should only be specified if a content match is required as part of the/ Uptime check. disabled: Whether the check is disabled or not. displayName: A human-friendly name for the Uptime check configuration. The display name should be unique within a Cloud Monitoring Workspace in order to make it easier to identify; however, uniqueness is not enforced. Required. httpCheck: Contains information needed to make an HTTP or HTTPS check. internalCheckers: The internal checkers that this check will egress from. If is_internal is true and this list is empty, the check will egress from all the InternalCheckers configured for the project that owns this UptimeCheckConfig. isInternal: If this is true, then checks are made only from the 'internal_checkers'. If it is false, then checks are made only from the 'selected_regions'. It is an error to provide 'selected_regions' when is_internal is true, or to provide 'internal_checkers' when is_internal is false. logCheckFailures: To specify whether to log the results of failed probes to Cloud Logging. monitoredResource: The monitored resource (https://cloud.google.com/monitoring/api/resources) associated with the configuration. The following monitored resource types are valid for this field: uptime_url, gce_instance, gae_app, aws_ec2_instance, aws_elb_load_balancer k8s_service servicedirectory_service cloud_run_revision name: Identifier. A unique resource name for this Uptime check configuration. The format is: projects/[PROJECT_ID_OR_NUMBER]/uptimeCheckConfigs/[UPTIME_CHECK_ID] [PROJECT_ID_OR_NUMBER] is the Workspace host project associated with the Uptime check.This field should be omitted when creating the Uptime check configuration; on create, the resource name is assigned by the server and included in the response. period: How often, in seconds, the Uptime check is performed. Currently, the only supported values are 60s (1 minute), 300s (5 minutes), 600s (10 minutes), and 900s (15 minutes). Optional, defaults to 60s. resourceGroup: The group resource associated with the configuration. selectedRegions: The list of regions from which the check will be run. Some regions contain one location, and others contain more than one. If this field is specified, enough regions must be provided to include a minimum of 3 locations. Not specifying this field will result in Uptime checks running from all available regions. syntheticMonitor: Specifies a Synthetic Monitor to invoke. tcpCheck: Contains information needed to make a TCP check. timeout: The maximum amount of time to wait for the request to complete (must be between 1 and 60 seconds). Required. userLabels: User-supplied key/value data to be used for organizing and identifying the UptimeCheckConfig objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter. """ class CheckerTypeValueValuesEnum(_messages.Enum): r"""The type of checkers to use to execute the Uptime check. Values: CHECKER_TYPE_UNSPECIFIED: The default checker type. Currently converted to STATIC_IP_CHECKERS on creation, the default conversion behavior may change in the future. STATIC_IP_CHECKERS: STATIC_IP_CHECKERS are used for uptime checks that perform egress across the public internet. STATIC_IP_CHECKERS use the static IP addresses returned by ListUptimeCheckIps. VPC_CHECKERS: VPC_CHECKERS are used for uptime checks that perform egress using Service Directory and private network access. When using VPC_CHECKERS, the monitored resource type must be servicedirectory_service. """ CHECKER_TYPE_UNSPECIFIED = 0 STATIC_IP_CHECKERS = 1 VPC_CHECKERS = 2 class SelectedRegionsValueListEntryValuesEnum(_messages.Enum): r"""SelectedRegionsValueListEntryValuesEnum enum type. Values: REGION_UNSPECIFIED: Default value if no region is specified. Will result in Uptime checks running from all regions. USA: Allows checks to run from locations within the United States of America. EUROPE: Allows checks to run from locations within the continent of Europe. SOUTH_AMERICA: Allows checks to run from locations within the continent of South America. ASIA_PACIFIC: Allows checks to run from locations within the Asia Pacific area (ex: Singapore). USA_OREGON: Allows checks to run from locations within the western United States of America USA_IOWA: Allows checks to run from locations within the central United States of America USA_VIRGINIA: Allows checks to run from locations within the eastern United States of America """ REGION_UNSPECIFIED = 0 USA = 1 EUROPE = 2 SOUTH_AMERICA = 3 ASIA_PACIFIC = 4 USA_OREGON = 5 USA_IOWA = 6 USA_VIRGINIA = 7 @encoding.MapUnrecognizedFields('additionalProperties') class UserLabelsValue(_messages.Message): r"""User-supplied key/value data to be used for organizing and identifying the UptimeCheckConfig objects.The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter. Messages: AdditionalProperty: An additional property for a UserLabelsValue object. Fields: additionalProperties: Additional properties of type UserLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a UserLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) checkerType = _messages.EnumField('CheckerTypeValueValuesEnum', 1) contentMatchers = _messages.MessageField('ContentMatcher', 2, repeated=True) disabled = _messages.BooleanField(3) displayName = _messages.StringField(4) httpCheck = _messages.MessageField('HttpCheck', 5) internalCheckers = _messages.MessageField('InternalChecker', 6, repeated=True) isInternal = _messages.BooleanField(7) logCheckFailures = _messages.BooleanField(8) monitoredResource = _messages.MessageField('MonitoredResource', 9) name = _messages.StringField(10) period = _messages.StringField(11) resourceGroup = _messages.MessageField('ResourceGroup', 12) selectedRegions = _messages.EnumField('SelectedRegionsValueListEntryValuesEnum', 13, repeated=True) syntheticMonitor = _messages.MessageField('SyntheticMonitorTarget', 14) tcpCheck = _messages.MessageField('TcpCheck', 15) timeout = _messages.StringField(16) userLabels = _messages.MessageField('UserLabelsValue', 17) class UptimeCheckIp(_messages.Message): r"""Contains the region, location, and list of IP addresses where checkers in the location run from. Enums: RegionValueValuesEnum: A broad region category in which the IP address is located. Fields: ipAddress: The IP address from which the Uptime check originates. This is a fully specified IP address (not an IP address range). Most IP addresses, as of this publication, are in IPv4 format; however, one should not rely on the IP addresses being in IPv4 format indefinitely, and should support interpreting this field in either IPv4 or IPv6 format. location: A more specific location within the region that typically encodes a particular city/town/metro (and its containing state/province or country) within the broader umbrella region category. region: A broad region category in which the IP address is located. """ class RegionValueValuesEnum(_messages.Enum): r"""A broad region category in which the IP address is located. Values: REGION_UNSPECIFIED: Default value if no region is specified. Will result in Uptime checks running from all regions. USA: Allows checks to run from locations within the United States of America. EUROPE: Allows checks to run from locations within the continent of Europe. SOUTH_AMERICA: Allows checks to run from locations within the continent of South America. ASIA_PACIFIC: Allows checks to run from locations within the Asia Pacific area (ex: Singapore). USA_OREGON: Allows checks to run from locations within the western United States of America USA_IOWA: Allows checks to run from locations within the central United States of America USA_VIRGINIA: Allows checks to run from locations within the eastern United States of America """ REGION_UNSPECIFIED = 0 USA = 1 EUROPE = 2 SOUTH_AMERICA = 3 ASIA_PACIFIC = 4 USA_OREGON = 5 USA_IOWA = 6 USA_VIRGINIA = 7 ipAddress = _messages.StringField(1) location = _messages.StringField(2) region = _messages.EnumField('RegionValueValuesEnum', 3) class ValueDescriptor(_messages.Message): r"""A descriptor for the value columns in a data point. Enums: MetricKindValueValuesEnum: The value stream kind. ValueTypeValueValuesEnum: The value type. Fields: key: The value key. metricKind: The value stream kind. unit: The unit in which time_series point values are reported. unit follows the UCUM format for units as seen in https://unitsofmeasure.org/ucum.html. unit is only valid if value_type is INTEGER, DOUBLE, DISTRIBUTION. valueType: The value type. """ class MetricKindValueValuesEnum(_messages.Enum): r"""The value stream kind. Values: METRIC_KIND_UNSPECIFIED: Do not use this default value. GAUGE: An instantaneous measurement of a value. DELTA: The change in a value during a time interval. CUMULATIVE: A value accumulated over a time interval. Cumulative measurements in a time series should have the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points. """ METRIC_KIND_UNSPECIFIED = 0 GAUGE = 1 DELTA = 2 CUMULATIVE = 3 class ValueTypeValueValuesEnum(_messages.Enum): r"""The value type. Values: VALUE_TYPE_UNSPECIFIED: Do not use this default value. BOOL: The value is a boolean. This value type can be used only if the metric kind is GAUGE. INT64: The value is a signed 64-bit integer. DOUBLE: The value is a double precision floating point number. STRING: The value is a text string. This value type can be used only if the metric kind is GAUGE. DISTRIBUTION: The value is a Distribution. MONEY: The value is money. """ VALUE_TYPE_UNSPECIFIED = 0 BOOL = 1 INT64 = 2 DOUBLE = 3 STRING = 4 DISTRIBUTION = 5 MONEY = 6 key = _messages.StringField(1) metricKind = _messages.EnumField('MetricKindValueValuesEnum', 2) unit = _messages.StringField(3) valueType = _messages.EnumField('ValueTypeValueValuesEnum', 4) class VerifyNotificationChannelRequest(_messages.Message): r"""The VerifyNotificationChannel request. Fields: code: Required. The verification code that was delivered to the channel as a result of invoking the SendNotificationChannelVerificationCode API method or that was retrieved from a verified channel via GetNotificationChannelVerificationCode. For example, one might have "G-123456" or "TKNZGhhd2EyN3I1MnRnMjRv" (in general, one is only guaranteed that the code is valid UTF-8; one should not make any assumptions regarding the structure or format of the code). """ code = _messages.StringField(1) class WindowsBasedSli(_messages.Message): r"""A WindowsBasedSli defines good_service as the count of time windows for which the provided service was of good quality. Criteria for determining if service was good are embedded in the window_criterion. Fields: goodBadMetricFilter: A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) specifying a TimeSeries with ValueType = BOOL. The window is good if any true values appear in the window. goodTotalRatioThreshold: A window is good if its performance is high enough. metricMeanInRange: A window is good if the metric's value is in a good range, averaged across returned streams. metricSumInRange: A window is good if the metric's value is in a good range, summed across returned streams. windowPeriod: Duration over which window quality is evaluated. Must be an integer fraction of a day and at least 60s. """ goodBadMetricFilter = _messages.StringField(1) goodTotalRatioThreshold = _messages.MessageField('PerformanceThreshold', 2) metricMeanInRange = _messages.MessageField('MetricRange', 3) metricSumInRange = _messages.MessageField('MetricRange', 4) windowPeriod = _messages.StringField(5) encoding.AddCustomJsonFieldMapping( StandardQueryParameters, 'f__xgafv', '$.xgafv') encoding.AddCustomJsonEnumMapping( StandardQueryParameters.FXgafvValueValuesEnum, '_1', '1') encoding.AddCustomJsonEnumMapping( StandardQueryParameters.FXgafvValueValuesEnum, '_2', '2') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'aggregation_alignmentPeriod', 'aggregation.alignmentPeriod') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'aggregation_crossSeriesReducer', 'aggregation.crossSeriesReducer') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'aggregation_groupByFields', 'aggregation.groupByFields') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'aggregation_perSeriesAligner', 'aggregation.perSeriesAligner') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'interval_endTime', 'interval.endTime') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'interval_startTime', 'interval.startTime') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'secondaryAggregation_alignmentPeriod', 'secondaryAggregation.alignmentPeriod') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'secondaryAggregation_crossSeriesReducer', 'secondaryAggregation.crossSeriesReducer') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'secondaryAggregation_groupByFields', 'secondaryAggregation.groupByFields') encoding.AddCustomJsonFieldMapping( MonitoringFoldersTimeSeriesListRequest, 'secondaryAggregation_perSeriesAligner', 'secondaryAggregation.perSeriesAligner') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'aggregation_alignmentPeriod', 'aggregation.alignmentPeriod') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'aggregation_crossSeriesReducer', 'aggregation.crossSeriesReducer') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'aggregation_groupByFields', 'aggregation.groupByFields') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'aggregation_perSeriesAligner', 'aggregation.perSeriesAligner') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'interval_endTime', 'interval.endTime') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'interval_startTime', 'interval.startTime') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'secondaryAggregation_alignmentPeriod', 'secondaryAggregation.alignmentPeriod') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'secondaryAggregation_crossSeriesReducer', 'secondaryAggregation.crossSeriesReducer') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'secondaryAggregation_groupByFields', 'secondaryAggregation.groupByFields') encoding.AddCustomJsonFieldMapping( MonitoringOrganizationsTimeSeriesListRequest, 'secondaryAggregation_perSeriesAligner', 'secondaryAggregation.perSeriesAligner') encoding.AddCustomJsonFieldMapping( MonitoringProjectsGroupsMembersListRequest, 'interval_endTime', 'interval.endTime') encoding.AddCustomJsonFieldMapping( MonitoringProjectsGroupsMembersListRequest, 'interval_startTime', 'interval.startTime') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'aggregation_alignmentPeriod', 'aggregation.alignmentPeriod') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'aggregation_crossSeriesReducer', 'aggregation.crossSeriesReducer') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'aggregation_groupByFields', 'aggregation.groupByFields') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'aggregation_perSeriesAligner', 'aggregation.perSeriesAligner') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'interval_endTime', 'interval.endTime') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'interval_startTime', 'interval.startTime') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'secondaryAggregation_alignmentPeriod', 'secondaryAggregation.alignmentPeriod') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'secondaryAggregation_crossSeriesReducer', 'secondaryAggregation.crossSeriesReducer') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'secondaryAggregation_groupByFields', 'secondaryAggregation.groupByFields') encoding.AddCustomJsonFieldMapping( MonitoringProjectsTimeSeriesListRequest, 'secondaryAggregation_perSeriesAligner', 'secondaryAggregation.perSeriesAligner')