View Source API Reference google_api_monitoring v0.55.0
Modules
API client metadata for GoogleApi.Monitoring.V3.
API calls for all endpoints tagged Folders.
API calls for all endpoints tagged Organizations.
API calls for all endpoints tagged Projects.
API calls for all endpoints tagged Services.
API calls for all endpoints tagged UptimeCheckIps.
Handle Tesla connections for GoogleApi.Monitoring.V3.
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).
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 alert policies, see Introduction to Alerting (https://cloud.google.com/monitoring/alerts/).
Control over how the notification channels in notification_channels are notified when this alert fires.
App Engine service. Learn more at https://cloud.google.com/appengine.
Future parameters for the availability SLI.
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.
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).
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.
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.
Cloud Endpoints service. Learn more at https://cloud.google.com/endpoints.
A Synthetic Monitor deployed to a Cloud Functions V2 instance.
Cloud Run service. Learn more at https://cloud.google.com/run.
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 collection of data points sent from a collectd-based plugin. See the collectd documentation for more information.
Describes the error status for payloads that were not written.
A single data point from a collectd-based plugin.
Describes the error status for values that were not written.
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.
The CreateCollectdTimeSeries request.
The CreateCollectdTimeSeries response.
The CreateTimeSeries request.
Summary of the result of a failed request to write data to a time series.
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.
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.
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.
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 content string and a MIME type that describes the content string's format.
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.
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); }
Detailed information about an error category.
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.
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.
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)).
A single field of a message type.
Options used when forecasting the time series and testing the predicted value against the threshold.
The GetNotificationChannelVerificationCode request.
The GetNotificationChannelVerificationCode request.
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 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).
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).
Range of numerical values within min and max.
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.
Information involved in an HTTP/HTTPS Uptime check request.
An internal checker allows Uptime checks to run on private/internal GCP resources.
Canonical service scoped to an Istio mesh. Anthos clusters running ASM >= 1.6.8 will have their services ingested as this type.
Information needed to perform a JSONPath content match. Used for ContentMatcherOption::MATCHES_JSON_PATH and ContentMatcherOption::NOT_MATCHES_JSON_PATH.
A description of a label.
A label value.
Parameters for a latency threshold SLI.
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)).
The protocol for the ListAlertPolicies response.
The ListGroupMembers response.
The ListGroups response.
The ListMetricDescriptors response.
The ListMonitoredResourceDescriptors response.
The ListNotificationChannelDescriptors response.
The ListNotificationChannels response.
The ListServiceLevelObjectives response.
The ListServices response.
The results of a successful ListSnoozes call, containing the matching Snoozes.
The ListTimeSeries response.
The protocol for the ListUptimeCheckConfigs response.
The protocol for the ListUptimeCheckIps response.
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.
Istio service scoped to an Istio mesh. Anthos clusters running ASM < 1.6.8 will have their services ingested as this type.
A specific metric, identified by specifying values for all of the labels of a MetricDescriptor.
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.
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.
Additional annotations that can be used to guide the usage of a metric.
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 condition type that compares a collection of time series against a threshold.
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 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.
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.
A condition type that allows alert policies to be defined using Monitoring Query Language (https://cloud.google.com/monitoring/mql).
Describes a change made to a configuration.
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.
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.
Control over how the notification channels in notification_channels are notified when this alert fires, on a per-channel basis.
Control over the rate of notifications sent to this alert policy's notification channels.
Contains metadata for longrunning operation for the edit Metrics Scope endpoints.
A protocol buffer option, which can be attached to a message, field, enumeration, etc.
A PerformanceThreshold is used when each window is good when that window has a sufficiently high performance.
Information involved in sending ICMP pings alongside public HTTP/TCP checks. For HTTP, the pings are performed for each part of the redirect chain.
A single data point in a time series.
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.
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).
The QueryTimeSeries request.
The QueryTimeSeries response.
The range of the population values.
Service Level Indicators for which atomic units of service are counted directly.
The resource submessage for group checks. It can be used instead of a monitored resource, when multiple resources are being monitored.
A status to accept. Either a status code class like "2xx", or an integer status code like "200".
The SendNotificationChannelVerificationCode request.
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.
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 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."
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.
SourceContext represents information about the source of a protobuf element, like the file in which it is defined.
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]
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 Synthetic Monitor to be invoked by Uptime.
Information required for a TCP Uptime check request.
Configuration for how to query telemetry on a Service.
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 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.
Represents the values of a time series associated with a TimeSeriesDescriptor.
A descriptor for the labels and points in a 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.
Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used.
A protocol buffer message type.
A single strongly-typed value.
This message configures which resources and services to monitor for availability.
Contains the region, location, and list of IP addresses where checkers in the location run from.
A descriptor for the value columns in a data point.
The VerifyNotificationChannel request.
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.