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<h1><a href="monitoring_v3.html">Cloud Monitoring API</a> . <a href="monitoring_v3.projects.html">projects</a> . <a href="monitoring_v3.projects.alertPolicies.html">alertPolicies</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
  <code><a href="#close">close()</a></code></p>
<p class="firstline">Close httplib2 connections.</p>
<p class="toc_element">
  <code><a href="#create">create(name, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Creates a new alerting policy.</p>
<p class="toc_element">
  <code><a href="#delete">delete(name, x__xgafv=None)</a></code></p>
<p class="firstline">Deletes an alerting policy.</p>
<p class="toc_element">
  <code><a href="#get">get(name, x__xgafv=None)</a></code></p>
<p class="firstline">Gets a single alerting policy.</p>
<p class="toc_element">
  <code><a href="#list">list(name, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)</a></code></p>
<p class="firstline">Lists the existing alerting policies for the workspace.</p>
<p class="toc_element">
  <code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<p class="toc_element">
  <code><a href="#patch">patch(name, body=None, updateMask=None, x__xgafv=None)</a></code></p>
<p class="firstline">Updates an alerting policy. You can either replace the entire policy with a new one or replace only certain fields in the current alerting policy by specifying the fields to be updated via updateMask. Returns the updated alerting policy.</p>
<h3>Method Details</h3>
<div class="method">
    <code class="details" id="close">close()</code>
  <pre>Close httplib2 connections.</pre>
</div>

<div class="method">
    <code class="details" id="create">create(name, body=None, x__xgafv=None)</code>
  <pre>Creates a new alerting policy.

Args:
  name: string, 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 workspace, 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. (required)
  body: object, The request body.
    The object takes the form of:

{ # A description of the conditions under which some aspect of your system is considered to be &quot;unhealthy&quot; 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/).
  &quot;combiner&quot;: &quot;A String&quot;, # 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.
  &quot;conditions&quot;: [ # 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.
    { # 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.
      &quot;conditionAbsent&quot;: { # 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 that checks that a time series continues to receive new data points.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;duration&quot;: &quot;A String&quot;, # 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. The Duration.nanos field is ignored.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionMonitoringQueryLanguage&quot;: { # A condition type that allows alert policies to be defined using Monitoring Query Language (https://cloud.google.com/monitoring/mql). # A condition that uses the Monitoring Query Language to define alerts.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;query&quot;: &quot;A String&quot;, # Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionThreshold&quot;: { # A condition type that compares a collection of time series against a threshold. # A condition that compares a time series against a threshold.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;comparison&quot;: &quot;A String&quot;, # 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.
        &quot;denominatorAggregations&quot;: [ # 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.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;denominatorFilter&quot;: &quot;A String&quot;, # 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.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;thresholdValue&quot;: 3.14, # A value against which to compare the time series.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple conditions in the same policy.
      &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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. Stackdriver 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.
    },
  ],
  &quot;creationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.
  &quot;documentation&quot;: { # A content string and a MIME type that describes the content string&#x27;s format. # 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.
    &quot;content&quot;: &quot;A String&quot;, # The text 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.
    &quot;mimeType&quot;: &quot;A String&quot;, # The format of the content field. Presently, only the value &quot;text/markdown&quot; is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
  },
  &quot;enabled&quot;: True or False, # 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.
  &quot;mutationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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.
  &quot;notificationChannels&quot;: [ # 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]
    &quot;A String&quot;,
  ],
  &quot;userLabels&quot;: { # 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.
    &quot;a_key&quot;: &quot;A String&quot;,
  },
  &quot;validity&quot;: { # 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). # Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the alert policy will not generate incidents.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # 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.
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A description of the conditions under which some aspect of your system is considered to be &quot;unhealthy&quot; 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/).
  &quot;combiner&quot;: &quot;A String&quot;, # 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.
  &quot;conditions&quot;: [ # 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.
    { # 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.
      &quot;conditionAbsent&quot;: { # 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 that checks that a time series continues to receive new data points.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;duration&quot;: &quot;A String&quot;, # 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. The Duration.nanos field is ignored.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionMonitoringQueryLanguage&quot;: { # A condition type that allows alert policies to be defined using Monitoring Query Language (https://cloud.google.com/monitoring/mql). # A condition that uses the Monitoring Query Language to define alerts.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;query&quot;: &quot;A String&quot;, # Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionThreshold&quot;: { # A condition type that compares a collection of time series against a threshold. # A condition that compares a time series against a threshold.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;comparison&quot;: &quot;A String&quot;, # 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.
        &quot;denominatorAggregations&quot;: [ # 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.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;denominatorFilter&quot;: &quot;A String&quot;, # 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.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;thresholdValue&quot;: 3.14, # A value against which to compare the time series.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple conditions in the same policy.
      &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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. Stackdriver 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.
    },
  ],
  &quot;creationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.
  &quot;documentation&quot;: { # A content string and a MIME type that describes the content string&#x27;s format. # 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.
    &quot;content&quot;: &quot;A String&quot;, # The text 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.
    &quot;mimeType&quot;: &quot;A String&quot;, # The format of the content field. Presently, only the value &quot;text/markdown&quot; is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
  },
  &quot;enabled&quot;: True or False, # 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.
  &quot;mutationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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.
  &quot;notificationChannels&quot;: [ # 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]
    &quot;A String&quot;,
  ],
  &quot;userLabels&quot;: { # 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.
    &quot;a_key&quot;: &quot;A String&quot;,
  },
  &quot;validity&quot;: { # 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). # Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the alert policy will not generate incidents.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # 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.
  },
}</pre>
</div>

<div class="method">
    <code class="details" id="delete">delete(name, x__xgafv=None)</code>
  <pre>Deletes an alerting policy.

Args:
  name: string, Required. The alerting policy to delete. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] For more information, see AlertPolicy. (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # 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); } The JSON representation for Empty is empty JSON object {}.
}</pre>
</div>

<div class="method">
    <code class="details" id="get">get(name, x__xgafv=None)</code>
  <pre>Gets a single alerting policy.

Args:
  name: string, Required. The alerting policy to retrieve. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID]  (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A description of the conditions under which some aspect of your system is considered to be &quot;unhealthy&quot; 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/).
  &quot;combiner&quot;: &quot;A String&quot;, # 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.
  &quot;conditions&quot;: [ # 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.
    { # 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.
      &quot;conditionAbsent&quot;: { # 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 that checks that a time series continues to receive new data points.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;duration&quot;: &quot;A String&quot;, # 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. The Duration.nanos field is ignored.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionMonitoringQueryLanguage&quot;: { # A condition type that allows alert policies to be defined using Monitoring Query Language (https://cloud.google.com/monitoring/mql). # A condition that uses the Monitoring Query Language to define alerts.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;query&quot;: &quot;A String&quot;, # Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionThreshold&quot;: { # A condition type that compares a collection of time series against a threshold. # A condition that compares a time series against a threshold.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;comparison&quot;: &quot;A String&quot;, # 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.
        &quot;denominatorAggregations&quot;: [ # 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.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;denominatorFilter&quot;: &quot;A String&quot;, # 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.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;thresholdValue&quot;: 3.14, # A value against which to compare the time series.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple conditions in the same policy.
      &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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. Stackdriver 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.
    },
  ],
  &quot;creationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.
  &quot;documentation&quot;: { # A content string and a MIME type that describes the content string&#x27;s format. # 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.
    &quot;content&quot;: &quot;A String&quot;, # The text 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.
    &quot;mimeType&quot;: &quot;A String&quot;, # The format of the content field. Presently, only the value &quot;text/markdown&quot; is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
  },
  &quot;enabled&quot;: True or False, # 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.
  &quot;mutationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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.
  &quot;notificationChannels&quot;: [ # 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]
    &quot;A String&quot;,
  ],
  &quot;userLabels&quot;: { # 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.
    &quot;a_key&quot;: &quot;A String&quot;,
  },
  &quot;validity&quot;: { # 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). # Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the alert policy will not generate incidents.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # 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.
  },
}</pre>
</div>

<div class="method">
    <code class="details" id="list">list(name, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)</code>
  <pre>Lists the existing alerting policies for the workspace.

Args:
  name: string, 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. (required)
  filter: string, 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).
  orderBy: string, 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: integer, The maximum number of results to return in a single response.
  pageToken: string, 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.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # The protocol for the ListAlertPolicies response.
  &quot;alertPolicies&quot;: [ # The returned alert policies.
    { # A description of the conditions under which some aspect of your system is considered to be &quot;unhealthy&quot; 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/).
      &quot;combiner&quot;: &quot;A String&quot;, # 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.
      &quot;conditions&quot;: [ # 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.
        { # 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.
          &quot;conditionAbsent&quot;: { # 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 that checks that a time series continues to receive new data points.
            &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
              { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
                &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
                &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
                &quot;groupByFields&quot;: [ # 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.
                  &quot;A String&quot;,
                ],
                &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
              },
            ],
            &quot;duration&quot;: &quot;A String&quot;, # 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. The Duration.nanos field is ignored.
            &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
            &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
              &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
              &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
            },
          },
          &quot;conditionMonitoringQueryLanguage&quot;: { # A condition type that allows alert policies to be defined using Monitoring Query Language (https://cloud.google.com/monitoring/mql). # A condition that uses the Monitoring Query Language to define alerts.
            &quot;duration&quot;: &quot;A String&quot;, # 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.
            &quot;query&quot;: &quot;A String&quot;, # Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
            &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
              &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
              &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
            },
          },
          &quot;conditionThreshold&quot;: { # A condition type that compares a collection of time series against a threshold. # A condition that compares a time series against a threshold.
            &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
              { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
                &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
                &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
                &quot;groupByFields&quot;: [ # 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.
                  &quot;A String&quot;,
                ],
                &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
              },
            ],
            &quot;comparison&quot;: &quot;A String&quot;, # 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.
            &quot;denominatorAggregations&quot;: [ # 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.
              { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
                &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
                &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
                &quot;groupByFields&quot;: [ # 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.
                  &quot;A String&quot;,
                ],
                &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
              },
            ],
            &quot;denominatorFilter&quot;: &quot;A String&quot;, # 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.
            &quot;duration&quot;: &quot;A String&quot;, # 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.
            &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
            &quot;thresholdValue&quot;: 3.14, # A value against which to compare the time series.
            &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
              &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
              &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
            },
          },
          &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple conditions in the same policy.
          &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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. Stackdriver 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.
        },
      ],
      &quot;creationRecord&quot;: { # Describes a change made to a configuration. # 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.
        &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
        &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
      },
      &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.
      &quot;documentation&quot;: { # A content string and a MIME type that describes the content string&#x27;s format. # 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.
        &quot;content&quot;: &quot;A String&quot;, # The text 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.
        &quot;mimeType&quot;: &quot;A String&quot;, # The format of the content field. Presently, only the value &quot;text/markdown&quot; is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
      },
      &quot;enabled&quot;: True or False, # 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.
      &quot;mutationRecord&quot;: { # Describes a change made to a configuration. # 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.
        &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
        &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
      },
      &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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.
      &quot;notificationChannels&quot;: [ # 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]
        &quot;A String&quot;,
      ],
      &quot;userLabels&quot;: { # 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.
        &quot;a_key&quot;: &quot;A String&quot;,
      },
      &quot;validity&quot;: { # 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). # Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the alert policy will not generate incidents.
        &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
        &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
          },
        ],
        &quot;message&quot;: &quot;A String&quot;, # 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.
      },
    },
  ],
  &quot;nextPageToken&quot;: &quot;A String&quot;, # 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.
  &quot;totalSize&quot;: 42, # 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
}</pre>
</div>

<div class="method">
    <code class="details" id="list_next">list_next(previous_request, previous_response)</code>
  <pre>Retrieves the next page of results.

Args:
  previous_request: The request for the previous page. (required)
  previous_response: The response from the request for the previous page. (required)

Returns:
  A request object that you can call &#x27;execute()&#x27; on to request the next
  page. Returns None if there are no more items in the collection.
    </pre>
</div>

<div class="method">
    <code class="details" id="patch">patch(name, body=None, updateMask=None, x__xgafv=None)</code>
  <pre>Updates an alerting policy. You can either replace the entire policy with a new one or replace only certain fields in the current alerting policy by specifying the fields to be updated via updateMask. Returns the updated alerting policy.

Args:
  name: string, 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 Stackdriver 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. (required)
  body: object, The request body.
    The object takes the form of:

{ # A description of the conditions under which some aspect of your system is considered to be &quot;unhealthy&quot; 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/).
  &quot;combiner&quot;: &quot;A String&quot;, # 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.
  &quot;conditions&quot;: [ # 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.
    { # 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.
      &quot;conditionAbsent&quot;: { # 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 that checks that a time series continues to receive new data points.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;duration&quot;: &quot;A String&quot;, # 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. The Duration.nanos field is ignored.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionMonitoringQueryLanguage&quot;: { # A condition type that allows alert policies to be defined using Monitoring Query Language (https://cloud.google.com/monitoring/mql). # A condition that uses the Monitoring Query Language to define alerts.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;query&quot;: &quot;A String&quot;, # Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionThreshold&quot;: { # A condition type that compares a collection of time series against a threshold. # A condition that compares a time series against a threshold.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;comparison&quot;: &quot;A String&quot;, # 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.
        &quot;denominatorAggregations&quot;: [ # 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.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;denominatorFilter&quot;: &quot;A String&quot;, # 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.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;thresholdValue&quot;: 3.14, # A value against which to compare the time series.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple conditions in the same policy.
      &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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. Stackdriver 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.
    },
  ],
  &quot;creationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.
  &quot;documentation&quot;: { # A content string and a MIME type that describes the content string&#x27;s format. # 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.
    &quot;content&quot;: &quot;A String&quot;, # The text 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.
    &quot;mimeType&quot;: &quot;A String&quot;, # The format of the content field. Presently, only the value &quot;text/markdown&quot; is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
  },
  &quot;enabled&quot;: True or False, # 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.
  &quot;mutationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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.
  &quot;notificationChannels&quot;: [ # 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]
    &quot;A String&quot;,
  ],
  &quot;userLabels&quot;: { # 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.
    &quot;a_key&quot;: &quot;A String&quot;,
  },
  &quot;validity&quot;: { # 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). # Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the alert policy will not generate incidents.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # 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.
  },
}

  updateMask: string, 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&#x27;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.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A description of the conditions under which some aspect of your system is considered to be &quot;unhealthy&quot; 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/).
  &quot;combiner&quot;: &quot;A String&quot;, # 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.
  &quot;conditions&quot;: [ # 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.
    { # 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.
      &quot;conditionAbsent&quot;: { # 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 that checks that a time series continues to receive new data points.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;duration&quot;: &quot;A String&quot;, # 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. The Duration.nanos field is ignored.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionMonitoringQueryLanguage&quot;: { # A condition type that allows alert policies to be defined using Monitoring Query Language (https://cloud.google.com/monitoring/mql). # A condition that uses the Monitoring Query Language to define alerts.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;query&quot;: &quot;A String&quot;, # Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;conditionThreshold&quot;: { # A condition type that compares a collection of time series against a threshold. # A condition that compares a time series against a threshold.
        &quot;aggregations&quot;: [ # 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 resrouces). 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.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;comparison&quot;: &quot;A String&quot;, # 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.
        &quot;denominatorAggregations&quot;: [ # 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.
          { # 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 &quot;the 95% latency across the average of all tasks in a cluster&quot;. 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).
            &quot;alignmentPeriod&quot;: &quot;A String&quot;, # 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.
            &quot;crossSeriesReducer&quot;: &quot;A String&quot;, # 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.
            &quot;groupByFields&quot;: [ # 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.
              &quot;A String&quot;,
            ],
            &quot;perSeriesAligner&quot;: &quot;A String&quot;, # 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.
          },
        ],
        &quot;denominatorFilter&quot;: &quot;A String&quot;, # 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.
        &quot;duration&quot;: &quot;A String&quot;, # 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.
        &quot;filter&quot;: &quot;A String&quot;, # 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 (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) (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.
        &quot;thresholdValue&quot;: 3.14, # A value against which to compare the time series.
        &quot;trigger&quot;: { # Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1} trigger is used. # 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.
          &quot;count&quot;: 42, # The absolute number of time series that must fail the predicate for the condition to be triggered.
          &quot;percent&quot;: 3.14, # The percentage of time series that must fail the predicate for the condition to be triggered.
        },
      },
      &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple conditions in the same policy.
      &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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. Stackdriver 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.
    },
  ],
  &quot;creationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;displayName&quot;: &quot;A String&quot;, # A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don&#x27;t use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.
  &quot;documentation&quot;: { # A content string and a MIME type that describes the content string&#x27;s format. # 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.
    &quot;content&quot;: &quot;A String&quot;, # The text 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.
    &quot;mimeType&quot;: &quot;A String&quot;, # The format of the content field. Presently, only the value &quot;text/markdown&quot; is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
  },
  &quot;enabled&quot;: True or False, # 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.
  &quot;mutationRecord&quot;: { # Describes a change made to a configuration. # 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.
    &quot;mutateTime&quot;: &quot;A String&quot;, # When the change occurred.
    &quot;mutatedBy&quot;: &quot;A String&quot;, # The email address of the user making the change.
  },
  &quot;name&quot;: &quot;A String&quot;, # 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 Stackdriver 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.
  &quot;notificationChannels&quot;: [ # 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]
    &quot;A String&quot;,
  ],
  &quot;userLabels&quot;: { # 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.
    &quot;a_key&quot;: &quot;A String&quot;,
  },
  &quot;validity&quot;: { # 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). # Read-only description of how the alert policy is invalid. OK if the alert policy is valid. If not OK, the alert policy will not generate incidents.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # 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.
  },
}</pre>
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