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gcp_monitoring_alert_policy

`alert_strategy`

Type: STRUCT
Provider name: alertStrategy
Description: Control over how this alerting policy’s notification channels are notified.

auto_close
Type: STRING
Provider name: autoClose
Description: If an alerting policy that was active has no data for this long, any open incidents will close
notification_channel_strategy
Type: UNORDERED_LIST_STRUCT
Provider name: notificationChannelStrategy
Description: Control how notifications will be sent out, on a per-channel basis.
- notification_channel_names
  Type: UNORDERED_LIST_STRING
  Provider name: notificationChannelNames
  Description: The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notification_channels field of this AlertPolicy. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
- renotify_interval
  Type: STRING
  Provider name: renotifyInterval
  Description: The frequency at which to send reminder notifications for open incidents.
notification_prompts
Type: UNORDERED_LIST_STRING
Provider name: notificationPrompts
Description: For log-based alert policies, the notification prompts is always OPENED. For non log-based alert policies, the notification prompts can be OPENED or OPENED, CLOSED.
notification_rate_limit
Type: STRUCT
Provider name: notificationRateLimit
Description: Required for log-based alerting policies, i.e. policies with a LogMatch condition.This limit is not implemented for alerting policies that do not have a LogMatch condition.
- period
  Type: STRING
  Provider name: period
  Description: Not more than one notification per period.

`ancestors`

Type: UNORDERED_LIST_STRING

`combiner`

Type: STRING
Provider name: combiner
Description: 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.
Possible values:

COMBINE_UNSPECIFIED - An unspecified combiner.
AND - Combine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources.
OR - Combine conditions using the logical OR operator. An incident is created if any of the listed conditions is met.
AND_WITH_MATCHING_RESOURCE - Combine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource.

`conditions`

Type: UNORDERED_LIST_STRUCT
Provider name: conditions
Description: A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. If condition_time_series_query_language is present, it must be the only condition. If condition_monitoring_query_language is present, it must be the only condition.

condition_absent
Type: STRUCT
Provider name: conditionAbsent
Description: A condition that checks that a time series continues to receive new data points.
- aggregations
  Type: UNORDERED_LIST_STRUCT
  Provider name: aggregations
  Description: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
  - alignment_period
    Type: STRING
    Provider name: alignmentPeriod
    Description: 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.
  - cross_series_reducer
    Type: STRING
    Provider name: crossSeriesReducer
    Description: 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.
    Possible values:
    - REDUCE_NONE - No cross-time series reduction. The output of the Aligner is returned.
    - REDUCE_MEAN - Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
    - REDUCE_MIN - Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_MAX - Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_SUM - Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_STDDEV - Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
    - REDUCE_COUNT - Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
    - REDUCE_COUNT_TRUE - Reduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
    - REDUCE_COUNT_FALSE - Reduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
    - REDUCE_FRACTION_TRUE - Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
    - REDUCE_PERCENTILE_99 - Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_95 - Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_50 - Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_05 - Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  - group_by_fields
    Type: UNORDERED_LIST_STRING
    Provider name: groupByFields
    Description: 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.
  - per_series_aligner
    Type: STRING
    Provider name: perSeriesAligner
    Description: 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.
    Possible values:
    - ALIGN_NONE - No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
    - ALIGN_DELTA - Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_RATE - Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or ‘delta over time’. Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by ‘rate’, you mean ‘percentage change’, see the ALIGN_PERCENT_CHANGE aligner instead.
    - ALIGN_INTERPOLATE - Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_NEXT_OLDER - Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MIN - Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MAX - Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MEAN - Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
    - ALIGN_COUNT - Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
    - ALIGN_SUM - Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_STDDEV - Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
    - ALIGN_COUNT_TRUE - Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
    - ALIGN_COUNT_FALSE - Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
    - ALIGN_FRACTION_TRUE - Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
    - ALIGN_PERCENTILE_99 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_95 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_50 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_05 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENT_CHANGE - Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
- duration
  Type: STRING
  Provider name: duration
  Description: 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.
- filter
  Type: STRING
  Provider name: filter
  Description: 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.
- trigger
  Type: STRUCT
  Provider name: trigger
  Description: 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.
  - count
    Type: INT32
    Provider name: count
    Description: The absolute number of time series that must fail the predicate for the condition to be triggered.
  - percent
    Type: DOUBLE
    Provider name: percent
    Description: The percentage of time series that must fail the predicate for the condition to be triggered.
condition_matched_log
Type: STRUCT
Provider name: conditionMatchedLog
Description: A condition that checks for log messages matching given constraints. If set, no other conditions can be present.
- filter
  Type: STRING
  Provider name: filter
  Description: Required. A logs-based filter. See Advanced Logs Queries (https://cloud.google.com/logging/docs/view/advanced-queries) for how this filter should be constructed.
- label_extractors
  Type: MAP_STRING_STRING
  Provider name: annotations
  Description: Annotations is an unstructured key-value map stored with a policy that may be set by external tools to store and retrieve arbitrary metadata. They are not queryable and should be preserved when modifying objects.
condition_monitoring_query_language
Type: STRUCT
Provider name: conditionMonitoringQueryLanguage
Description: A condition that uses the Monitoring Query Language to define alerts.
- duration
  Type: STRING
  Provider name: duration
  Description: 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.
- evaluation_missing_data
  Type: STRING
  Provider name: evaluationMissingData
  Description: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.
  Possible values:
  - EVALUATION_MISSING_DATA_UNSPECIFIED - An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
  - EVALUATION_MISSING_DATA_INACTIVE - If there is no data to evaluate the condition, then evaluate the condition as false.
  - EVALUATION_MISSING_DATA_ACTIVE - If there is no data to evaluate the condition, then evaluate the condition as true.
  - EVALUATION_MISSING_DATA_NO_OP - Do not evaluate the condition to any value if there is no data.
- query
  Type: STRING
  Provider name: query
  Description: Monitoring Query Language (https://cloud.google.com/monitoring/mql) query that outputs a boolean stream.
- trigger
  Type: STRUCT
  Provider name: trigger
  Description: 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.
  - count
    Type: INT32
    Provider name: count
    Description: The absolute number of time series that must fail the predicate for the condition to be triggered.
  - percent
    Type: DOUBLE
    Provider name: percent
    Description: The percentage of time series that must fail the predicate for the condition to be triggered.
condition_prometheus_query_language
Type: STRUCT
Provider name: conditionPrometheusQueryLanguage
Description: A condition that uses the Prometheus query language to define alerts.
- alert_rule
  Type: STRING
  Provider name: alertRule
  Description: Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must be a valid Prometheus label name (https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels). This field may not exceed 2048 Unicode characters in length.
- disable_metric_validation
  Type: BOOLEAN
  Provider name: disableMetricValidation
  Description: Optional. Whether to disable metric existence validation for this condition.This allows alerting policies to be defined on metrics that do not yet exist, improving advanced customer workflows such as configuring alerting policies using Terraform.Users with the monitoring.alertPolicyViewer role are able to see the name of the non-existent metric in the alerting policy condition.
- duration
  Type: STRING
  Provider name: duration
  Description: Optional. Alerts are considered firing once their PromQL expression was evaluated to be “true” for this long. Alerts whose PromQL expression was not evaluated to be “true” for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.
- evaluation_interval
  Type: STRING
  Provider name: evaluationInterval
  Description: Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.
- query
  Type: STRING
  Provider name: query
  Description: Required. The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty.
- rule_group
  Type: STRING
  Provider name: ruleGroup
  Description: Optional. The rule group name of this alert in the corresponding Prometheus configuration file.Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future.This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length.
condition_sql
Type: STRUCT
Provider name: conditionSql
Description: A condition that periodically evaluates a SQL query result.
- boolean_test
  Type: STRUCT
  Provider name: booleanTest
  Description: Test the boolean value in the indicated column.
  - column
    Type: STRING
    Provider name: column
    Description: Required. The name of the column containing the boolean value. If the value in a row is NULL, that row is ignored.
- daily
  Type: STRUCT
  Provider name: daily
  Description: Schedule the query to execute every so many days.
  - execution_time
    Type: STRUCT
    Provider name: executionTime
    Description: Optional. The time of day (in UTC) at which the query should run. If left unspecified, the server picks an arbitrary time of day and runs the query at the same time each day.
    - hours
      Type: INT32
      Provider name: hours
      Description: Hours of a day in 24 hour format. Must be greater than or equal to 0 and typically must be less than or equal to 23. An API may choose to allow the value “24:00:00” for scenarios like business closing time.
    - minutes
      Type: INT32
      Provider name: minutes
      Description: Minutes of an hour. Must be greater than or equal to 0 and less than or equal to 59.
    - nanos
      Type: INT32
      Provider name: nanos
      Description: Fractions of seconds, in nanoseconds. Must be greater than or equal to 0 and less than or equal to 999,999,999.
    - seconds
      Type: INT32
      Provider name: seconds
      Description: Seconds of a minute. Must be greater than or equal to 0 and typically must be less than or equal to 59. An API may allow the value 60 if it allows leap-seconds.
  - periodicity
    Type: INT32
    Provider name: periodicity
    Description: Required. The number of days between runs. Must be greater than or equal to 1 day and less than or equal to 31 days.
- hourly
  Type: STRUCT
  Provider name: hourly
  Description: Schedule the query to execute every so many hours.
  - minute_offset
    Type: INT32
    Provider name: minuteOffset
    Description: Optional. The number of minutes after the hour (in UTC) to run the query. Must be greater than or equal to 0 minutes and less than or equal to 59 minutes. If left unspecified, then an arbitrary offset is used.
  - periodicity
    Type: INT32
    Provider name: periodicity
    Description: Required. The number of hours between runs. Must be greater than or equal to 1 hour and less than or equal to 48 hours.
- minutes
  Type: STRUCT
  Provider name: minutes
  Description: Schedule the query to execute every so many minutes.
  - periodicity
    Type: INT32
    Provider name: periodicity
    Description: Required. Number of minutes between runs. The interval must be greater than or equal to 5 minutes and less than or equal to 1440 minutes.
- query
  Type: STRING
  Provider name: query
  Description: Required. The Log Analytics SQL query to run, as a string. The query must conform to the required shape. Specifically, the query must not try to filter the input by time. A filter will automatically be applied to filter the input so that the query receives all rows received since the last time the query was run.For example, the following query extracts all log entries containing an HTTP request: SELECT timestamp, log_name, severity, http_request, resource, labels FROM my-project.global._Default._AllLogs WHERE http_request IS NOT NULL
- row_count_test
  Type: STRUCT
  Provider name: rowCountTest
  Description: Test the row count against a threshold.
  - comparison
    Type: STRING
    Provider name: comparison
    Description: Required. The comparison to apply between the number of rows returned by the query and the threshold.
    Possible values:
    - COMPARISON_UNSPECIFIED - No ordering relationship is specified.
    - COMPARISON_GT - True if the left argument is greater than the right argument.
    - COMPARISON_GE - True if the left argument is greater than or equal to the right argument.
    - COMPARISON_LT - True if the left argument is less than the right argument.
    - COMPARISON_LE - True if the left argument is less than or equal to the right argument.
    - COMPARISON_EQ - True if the left argument is equal to the right argument.
    - COMPARISON_NE - True if the left argument is not equal to the right argument.
  - threshold
    Type: INT64
    Provider name: threshold
    Description: Required. The value against which to compare the row count.
condition_threshold
Type: STRUCT
Provider name: conditionThreshold
Description: A condition that compares a time series against a threshold.
- aggregations
  Type: UNORDERED_LIST_STRUCT
  Provider name: aggregations
  Description: Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified.This field is similar to the one in the ListTimeSeries request (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list). It is advisable to use the ListTimeSeries method when debugging this field.
  - alignment_period
    Type: STRING
    Provider name: alignmentPeriod
    Description: 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.
  - cross_series_reducer
    Type: STRING
    Provider name: crossSeriesReducer
    Description: 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.
    Possible values:
    - REDUCE_NONE - No cross-time series reduction. The output of the Aligner is returned.
    - REDUCE_MEAN - Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
    - REDUCE_MIN - Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_MAX - Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_SUM - Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_STDDEV - Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
    - REDUCE_COUNT - Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
    - REDUCE_COUNT_TRUE - Reduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
    - REDUCE_COUNT_FALSE - Reduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
    - REDUCE_FRACTION_TRUE - Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
    - REDUCE_PERCENTILE_99 - Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_95 - Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_50 - Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_05 - Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  - group_by_fields
    Type: UNORDERED_LIST_STRING
    Provider name: groupByFields
    Description: 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.
  - per_series_aligner
    Type: STRING
    Provider name: perSeriesAligner
    Description: 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.
    Possible values:
    - ALIGN_NONE - No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
    - ALIGN_DELTA - Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_RATE - Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or ‘delta over time’. Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by ‘rate’, you mean ‘percentage change’, see the ALIGN_PERCENT_CHANGE aligner instead.
    - ALIGN_INTERPOLATE - Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_NEXT_OLDER - Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MIN - Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MAX - Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MEAN - Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
    - ALIGN_COUNT - Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
    - ALIGN_SUM - Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_STDDEV - Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
    - ALIGN_COUNT_TRUE - Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
    - ALIGN_COUNT_FALSE - Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
    - ALIGN_FRACTION_TRUE - Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
    - ALIGN_PERCENTILE_99 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_95 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_50 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_05 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENT_CHANGE - Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
- comparison
  Type: STRING
  Provider name: comparison
  Description: 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.
  Possible values:
  - COMPARISON_UNSPECIFIED - No ordering relationship is specified.
  - COMPARISON_GT - True if the left argument is greater than the right argument.
  - COMPARISON_GE - True if the left argument is greater than or equal to the right argument.
  - COMPARISON_LT - True if the left argument is less than the right argument.
  - COMPARISON_LE - True if the left argument is less than or equal to the right argument.
  - COMPARISON_EQ - True if the left argument is equal to the right argument.
  - COMPARISON_NE - True if the left argument is not equal to the right argument.
- denominator_aggregations
  Type: UNORDERED_LIST_STRUCT
  Provider name: denominatorAggregations
  Description: 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.
  - alignment_period
    Type: STRING
    Provider name: alignmentPeriod
    Description: 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.
  - cross_series_reducer
    Type: STRING
    Provider name: crossSeriesReducer
    Description: 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.
    Possible values:
    - REDUCE_NONE - No cross-time series reduction. The output of the Aligner is returned.
    - REDUCE_MEAN - Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
    - REDUCE_MIN - Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_MAX - Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_SUM - Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
    - REDUCE_STDDEV - Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
    - REDUCE_COUNT - Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
    - REDUCE_COUNT_TRUE - Reduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
    - REDUCE_COUNT_FALSE - Reduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
    - REDUCE_FRACTION_TRUE - Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
    - REDUCE_PERCENTILE_99 - Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_95 - Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_50 - Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
    - REDUCE_PERCENTILE_05 - Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  - group_by_fields
    Type: UNORDERED_LIST_STRING
    Provider name: groupByFields
    Description: 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.
  - per_series_aligner
    Type: STRING
    Provider name: perSeriesAligner
    Description: 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.
    Possible values:
    - ALIGN_NONE - No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
    - ALIGN_DELTA - Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_RATE - Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or ‘delta over time’. Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by ‘rate’, you mean ‘percentage change’, see the ALIGN_PERCENT_CHANGE aligner instead.
    - ALIGN_INTERPOLATE - Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_NEXT_OLDER - Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MIN - Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MAX - Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_MEAN - Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
    - ALIGN_COUNT - Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
    - ALIGN_SUM - Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
    - ALIGN_STDDEV - Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
    - ALIGN_COUNT_TRUE - Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
    - ALIGN_COUNT_FALSE - Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
    - ALIGN_FRACTION_TRUE - Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
    - ALIGN_PERCENTILE_99 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_95 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_50 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENTILE_05 - Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
    - ALIGN_PERCENT_CHANGE - Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.
- denominator_filter
  Type: STRING
  Provider name: denominatorFilter
  Description: A filter (https://cloud.google.com/monitoring/api/v3/filters) that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a denominator_filter is specified, the time series specified by the filter field will be used as the numerator.The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length.
- duration
  Type: STRING
  Provider name: duration
  Description: 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.
- evaluation_missing_data
  Type: STRING
  Provider name: evaluationMissingData
  Description: A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. To use this control, the value of the duration field must be greater than or equal to 60 seconds.
  Possible values:
  - EVALUATION_MISSING_DATA_UNSPECIFIED - An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
  - EVALUATION_MISSING_DATA_INACTIVE - If there is no data to evaluate the condition, then evaluate the condition as false.
  - EVALUATION_MISSING_DATA_ACTIVE - If there is no data to evaluate the condition, then evaluate the condition as true.
  - EVALUATION_MISSING_DATA_NO_OP - Do not evaluate the condition to any value if there is no data.
- filter
  Type: STRING
  Provider name: filter
  Description: 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.
- forecast_options
  Type: STRUCT
  Provider name: forecastOptions
  Description: When this field is present, the MetricThreshold condition forecasts whether the time series is predicted to violate the threshold within the forecast_horizon. When this field is not set, the MetricThreshold tests the current value of the timeseries against the threshold.
  - forecast_horizon
    Type: STRING
    Provider name: forecastHorizon
    Description: Required. The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured duration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.
- threshold_value
  Type: DOUBLE
  Provider name: thresholdValue
  Description: A value against which to compare the time series.
- trigger
  Type: STRUCT
  Provider name: trigger
  Description: 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.
  - count
    Type: INT32
    Provider name: count
    Description: The absolute number of time series that must fail the predicate for the condition to be triggered.
  - percent
    Type: DOUBLE
    Provider name: percent
    Description: The percentage of time series that must fail the predicate for the condition to be triggered.
gcp_display_name
Type: STRING
Provider name: displayName
Description: A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don’t use the same display name for multiple conditions in the same policy.
name
Type: STRING
Provider name: name
Description: Required if the condition exists. The unique resource name for this condition. Its format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID] [CONDITION_ID] is assigned by Cloud Monitoring when the condition is created as part of a new or updated alerting policy.When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.

`creation_record`

Type: STRUCT
Provider name: creationRecord
Description: 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.

mutate_time
Type: TIMESTAMP
Provider name: mutateTime
Description: When the change occurred.
mutated_by
Type: STRING
Provider name: mutatedBy
Description: The email address of the user making the change.

`documentation`

Type: STRUCT
Provider name: documentation
Description: 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.

content
Type: STRING
Provider name: content
Description: The body of the documentation, interpreted according to mime_type. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller. This text can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables#doc-vars).
links
Type: UNORDERED_LIST_STRUCT
Provider name: links
Description: Optional. Links to content such as playbooks, repositories, and other resources. This field can contain up to 3 entries.
- gcp_display_name
  Type: STRING
  Provider name: displayName
  Description: A short display name for the link. The display name must not be empty or exceed 63 characters. Example: “playbook”.
- url
  Type: STRING
  Provider name: url
  Description: The url of a webpage. A url can be templatized by using variables in the path or the query parameters. The total length of a URL should not exceed 2083 characters before and after variable expansion. Example: “https://my_domain.com/playbook?name=${resource.name}"
mime_type
Type: STRING
Provider name: mimeType
Description: The format of the content field. Presently, only the value “text/markdown” is supported. See Markdown (https://en.wikipedia.org/wiki/Markdown) for more information.
subject
Type: STRING
Provider name: subject
Description: Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread (https://stackoverflow.com/questions/1592291/what-is-the-email-subject-length-limit). It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255).The contents of the subject line can be templatized by using variables (https://cloud.google.com/monitoring/alerts/doc-variables#doc-vars). If this field is missing or empty, a default subject line will be generated.

`enabled`

Type: BOOLEAN
Provider name: enabled
Description: 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.

`gcp_display_name`

Type: STRING
Provider name: displayName
Description: A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don’t use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters.The convention for the display_name of a PrometheusQueryLanguageCondition is “{rule group name}/{alert name}”, where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the display_name is not a unique key of the AlertPolicy.

`labels`

Type: UNORDERED_LIST_STRING

`mutation_record`

Type: STRUCT
Provider name: mutationRecord
Description: 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.

mutate_time
Type: TIMESTAMP
Provider name: mutateTime
Description: When the change occurred.
mutated_by
Type: STRING
Provider name: mutatedBy
Description: The email address of the user making the change.

`name`

Type: STRING
Provider name: name
Description: Identifier. Required if the policy exists. The resource name for this policy. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] [ALERT_POLICY_ID] is assigned by Cloud Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request.

`notification_channels`

Type: UNORDERED_LIST_STRING
Provider name: notificationChannels
Description: 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]

`organization_id`

Type: STRING

`parent`

Type: STRING

`project_id`

Type: STRING

`project_number`

Type: STRING

`resource_name`

Type: STRING

`severity`

Type: STRING
Provider name: severity
Description: Optional. The severity of an alerting policy indicates how important incidents generated by that policy are. The severity level will be displayed on the Incident detail page and in notifications.
Possible values:

SEVERITY_UNSPECIFIED - No severity is specified. This is the default value.
CRITICAL - This is the highest severity level. Use this if the problem could cause significant damage or downtime.
ERROR - This is the medium severity level. Use this if the problem could cause minor damage or downtime.
WARNING - This is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future.

`tags`

Type: UNORDERED_LIST_STRING

`user_labels`

Type: MAP_STRING_STRING
Provider name: annotations
Description: Annotations is an unstructured key-value map stored with a policy that may be set by external tools to store and retrieve arbitrary metadata. They are not queryable and should be preserved when modifying objects.

`validity`

Type: STRUCT
Provider name: validity
Description: Read-only description of how the alerting policy is invalid. This field is only set when the alerting policy is invalid. An invalid alerting policy will not generate incidents.

code
Type: INT32
Provider name: code
Description: The status code, which should be an enum value of google.rpc.Code.
message
Type: STRING
Provider name: message
Description: 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.