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).
Preview: An identifier for an aggregation function. Aggregation functions are SQL functions that group or transform data from multiple points to a single point. This is a preview feature and may be subject to change before final release.
Preview: An identifier for an aggregation function. Aggregation functions are SQL functions that group or transform data from multiple points to a single point. This is a preview feature and may be subject to change before final release.
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).
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).
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).
Creates a new alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.
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.
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.
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.
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.
Preview: A breakdown is an aggregation applied to the measures over a specified column. A breakdown can result in multiple series across a category for the provided measure. This is a preview feature and may be subject to change before final release.
Preview: A breakdown is an aggregation applied to the measures over a specified column. A breakdown can result in multiple series across a category for the provided measure. This is a preview feature and may be subject to change before final release.
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.
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.
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.
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.
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.
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.
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.
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.
Creates a new custom dashboard. For examples on how you can use this API to create dashboards, see Managing dashboards by API (https://cloud.google.com/monitoring/dashboards/api-dashboard). This method requires the monitoring.dashboards.create permission on the specified project. For more information about permissions, see Cloud Identity and Access Management (https://cloud.google.com/iam).
Preview: A chart dimension for an SQL query. This is applied over the x-axis. This is a preview feature and may be subject to change before final release.
Preview: A chart dimension for an SQL query. This is applied over the x-axis. This is a preview feature and may be subject to change before final release.
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.
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.
A gauge chart shows where the current value sits within a pre-defined range. The upper and lower bounds should define the possible range of values for the scorecard's query (inclusive).
A gauge chart shows where the current value sits within a pre-defined range. The upper and lower bounds should define the possible range of values for the scorecard's query (inclusive).
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).
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).
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).
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).
Information needed to perform a JSONPath content match. Used for ContentMatcherOption::MATCHES_JSON_PATH and ContentMatcherOption::NOT_MATCHES_JSON_PATH.
Information needed to perform a JSONPath content match. Used for ContentMatcherOption::MATCHES_JSON_PATH and ContentMatcherOption::NOT_MATCHES_JSON_PATH.
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.
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.
Preview: A chart measure for an SQL query. This is applied over the y-axis. This is a preview feature and may be subject to change before final release.
Preview: A chart measure for an SQL query. This is applied over the y-axis. This is a preview feature and may be subject to change before final release.
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.
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.
Creates a new metric descriptor. The creation is executed asynchronously. User-created metric descriptors define custom metrics (https://cloud.google.com/monitoring/custom-metrics). The metric descriptor is updated if it already exists, except that metric labels are never removed.
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.
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.
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" }}
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" }}
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" }}
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" }}
A mosaic layout divides the available space into a grid of blocks, and overlays the grid with tiles. Unlike GridLayout, tiles may span multiple grid blocks and can be placed at arbitrary locations in the grid.
A mosaic layout divides the available space into a grid of blocks, and overlays the grid with tiles. Unlike GridLayout, tiles may span multiple grid blocks and can be placed at arbitrary locations in the grid.
Creates a new notification channel, representing a single notification endpoint such as an email address, SMS number, or PagerDuty service.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.
Preview: A query that produces an aggregated response and supporting data. This is a preview feature and may be subject to change before final release.
Preview: A query that produces an aggregated response and supporting data. This is a preview feature and may be subject to change before final release.
Describes a ranking-based time series filter. Each input time series is ranked with an aligner. The filter will allow up to num_time_series time series to pass through it, selecting them based on the relative ranking.For example, if ranking_method is METHOD_MEAN,direction is BOTTOM, and num_time_series is 3, then the 3 times series with the lowest mean values will pass through the filter.
Describes a ranking-based time series filter. Each input time series is ranked with an aligner. The filter will allow up to num_time_series time series to pass through it, selecting them based on the relative ranking.For example, if ranking_method is METHOD_MEAN,direction is BOTTOM, and num_time_series is 3, then the 3 times series with the lowest mean values will pass through the filter.
A condition type that allows alert 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/configuration/alerting_rules/). The semantics of a Prometheus alerting rule is described here (https://prometheus.io/docs/prometheus/latest/configuration/recording_rules/#rule).A Prometheus rule group is described here (https://prometheus.io/docs/prometheus/latest/configuration/recording_rules/). The semantics of a Prometheus rule group is described here (https://prometheus.io/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).
A condition type that allows alert 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/configuration/alerting_rules/). The semantics of a Prometheus alerting rule is described here (https://prometheus.io/docs/prometheus/latest/configuration/recording_rules/#rule).A Prometheus rule group is described here (https://prometheus.io/docs/prometheus/latest/configuration/recording_rules/). The semantics of a Prometheus rule group is described here (https://prometheus.io/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).
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.
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.
Creates a Snooze that will prevent alerts, which match the provided criteria, from being opened. The Snooze applies for a specific time interval.
Note - this resource's API doesn't support deletion. When deleted, the resource will persist
on Google Cloud even though it will be deleted from Pulumi state.
A sparkChart is a small chart suitable for inclusion in a table-cell or inline in text. This message contains the configuration for a sparkChart to show up on a Scorecard, showing recent trends of the scorecard's timeseries.
A sparkChart is a small chart suitable for inclusion in a table-cell or inline in text. This message contains the configuration for a sparkChart to show up on a Scorecard, showing recent trends of the scorecard's timeseries.
A filter that ranks streams based on their statistical relation to other streams in a request. Note: This field is deprecated and completely ignored by the API.
A filter that ranks streams based on their statistical relation to other streams in a request. Note: This field is deprecated and completely ignored by the API.
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).
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).
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#MetricKind) 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.
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#MetricKind) 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.
A pair of time series filters that define a ratio computation. The output time series is the pair-wise division of each aligned element from the numerator and denominator time series.
A pair of time series filters that define a ratio computation. The output time series is the pair-wise division of each aligned element from the numerator and denominator time series.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Optional. The severity of an alert policy indicates how important alerts generated by that policy are. The severity level, if specified, will be displayed on the Incident detail page and in notifications.
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.
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.
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.
rankingMethod is applied to a set of time series, and then the produced value for each individual time series is used to compare a given time series to others. These are methods that cannot be applied stream-by-stream, but rather require the full context of a request to evaluate time series.
Fetches a specific dashboard.This method requires the monitoring.dashboards.get permission on the specified dashboard. For more information, see Cloud Identity and Access Management (https://cloud.google.com/iam).
args Arguments passed to this invoke. Arguments for getDashboard.
options Invoke options controlling this call.
Gets a single metric descriptor.
args Arguments passed to this invoke. Arguments for getMetricDescriptor.
options Invoke options controlling this call.
Gets a single notification channel. The channel includes the relevant configuration details with which the channel was created. However, the response may truncate or omit passwords, API keys, or other private key matter and thus the response may not be 100% identical to the information that was supplied in the call to the create method.
args Arguments passed to this invoke. Arguments for getNotificationChannel.
options Invoke options controlling this call.
Get a ServiceLevelObjective by name.
args Arguments passed to this invoke. Arguments for getServiceLevelObjective.
options Invoke options controlling this call.
Gets a single Uptime check configuration.
args Arguments passed to this invoke. Arguments for getUptimeCheckConfig.
options Invoke options controlling this call.
