REST Resource: projects.alertPolicies

Resource: AlertPolicy

A description of the conditions under which some aspect of your system is considered to be "unhealthy" and the ways to notify people or services about this state. For an overview of alerting policies, seeIntroduction to Alerting.

JSON representation
{"name":string,"displayName":string,"documentation":{object (Documentation)},"userLabels":{string:string,...},"conditions":[{object (Condition)}],"combiner":enum (ConditionCombinerType),"enabled":boolean,"validity":{object (Status)},"notificationChannels":[string],"creationRecord":{object (MutationRecord)},"mutationRecord":{object (MutationRecord)},"alertStrategy":{object (AlertStrategy)},"severity":enum (Severity)}
Fields
name

string

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 thealertPolicies.create method, do not include thename field in the alerting policy passed as part of the request.

displayName

string

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 displayName 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 displayName is not a unique key of the AlertPolicy.

documentation

object (Documentation)

Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation.

userLabels

map (key: string, value: string)

User-supplied key/value data to be used for organizing and identifying theAlertPolicy objects.

The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter.

Note that Prometheus {alert name} is avalid Prometheus label names, whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values.

conditions[]

object (Condition)

A list of conditions for the policy. The conditions are combined by AND or OR according to thecombiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions. IfconditionTimeSeriesQueryLanguage is present, it must be the onlycondition. IfconditionMonitoringQueryLanguage is present, it must be the onlycondition.

combiner

enum (ConditionCombinerType)

How to combine the results of multiple conditions to determine if an incident should be opened. IfconditionTimeSeriesQueryLanguage is present, this must beCOMBINE_UNSPECIFIED.

enabled

boolean

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.

validity

object (Status)

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.

notificationChannels[]

string

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 thename field in each of theNotificationChannel objects that are returned from thenotificationChannels.list method. The format of the entries in this field is:

projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
creationRecord

object (MutationRecord)

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.

mutationRecord

object (MutationRecord)

A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored.

alertStrategy

object (AlertStrategy)

Control over how this alerting policy's notification channels are notified.

severity

enum (Severity)

Optional. The severity of an alerting policy indicates how important incidents generated by that policy are. The severity level will be displayed on the Incident detail page and in notifications.

Documentation

Documentation that is included in the notifications and incidents pertaining to this policy.

JSON representation
{"content":string,"mimeType":string,"subject":string,"links":[{object (Link)}]}
Fields
content

string

The body of the documentation, interpreted according tomimeType. 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 betemplatized by using variables.

mimeType

string

The format of thecontent field. Presently, only the value"text/markdown" is supported. SeeMarkdown for more information.

subject

string

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 bythis thread. 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 betemplatized by using variables. If this field is missing or empty, a default subject line will be generated.

links[]

object (Link)

Optional. Links to content such as playbooks, repositories, and other resources. This field can contain up to 3 entries.

Link

Links to content such as playbooks, repositories, and other resources.

JSON representation
{"displayName":string,"url":string}
Fields
displayName

string

A short display name for the link. The display name must not be empty or exceed 63 characters. Example: "playbook".

url

string

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}"

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.

JSON representation
{"name":string,"displayName":string,// Union fieldcondition can be only one of the following:"conditionThreshold":{object (MetricThreshold)},"conditionAbsent":{object (MetricAbsence)},"conditionMatchedLog":{object (LogMatch)},"conditionMonitoringQueryLanguage":{object (MonitoringQueryLanguageCondition)},"conditionPrometheusQueryLanguage":{object (PrometheusQueryLanguageCondition)},"conditionSql":{object (SqlCondition)}// End of list of possible types for union fieldcondition.}
Fields
name

string

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 thealertPolicies.create method, do not include thename field in the conditions of the requested alerting policy. Cloud Monitoring creates the condition identifiers and includes them in the new policy.

When calling thealertPolicies.update method to update a policy, including a conditionname 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.

displayName

string

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.

Union fieldcondition. Only one of the following condition types will be specified.condition can be only one of the following:
conditionThreshold

object (MetricThreshold)

A condition that compares a time series against a threshold.

conditionAbsent

object (MetricAbsence)

A condition that checks that a time series continues to receive new data points.

conditionMatchedLog

object (LogMatch)

A condition that checks for log messages matching given constraints. If set, no other conditions can be present.

conditionMonitoringQueryLanguage

object (MonitoringQueryLanguageCondition)

A condition that uses the Monitoring Query Language to define alerts.

conditionPrometheusQueryLanguage

object (PrometheusQueryLanguageCondition)

A condition that uses the Prometheus query language to define alerts.

conditionSql

object (SqlCondition)

A condition that periodically evaluates a SQL query result.

MetricThreshold

A condition type that compares a collection of time series against a threshold.

JSON representation
{"filter":string,"aggregations":[{object (Aggregation)}],"denominatorFilter":string,"denominatorAggregations":[{object (Aggregation)}],"forecastOptions":{object (ForecastOptions)},"comparison":enum (ComparisonType),"thresholdValue":number,"duration":string,"trigger":{object (Trigger)},"evaluationMissingData":enum (EvaluationMissingData)}
Fields
filter

string

Required. Afilter that identifies which time series should be compared with the threshold.

The filter is similar to the one that is specified in thetimeSeries.list request (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.

aggregations[]

object (Aggregation)

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 thetimeSeries.list request. It is advisable to use thetimeSeries.list method when debugging this field.

denominatorFilter

string

Afilter that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If adenominatorFilter is specified, the time series specified by thefilter 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.

denominatorAggregations[]

object (Aggregation)

Specifies the alignment of data points in individual time series selected bydenominatorFilter 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, theaggregations anddenominatorAggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.

forecastOptions

object (ForecastOptions)

When this field is present, theMetricThreshold condition forecasts whether the time series is predicted to violate the threshold within theforecastHorizon. When this field is not set, theMetricThreshold tests the current value of the timeseries against the threshold.

comparison

enum (ComparisonType)

The comparison to apply between the time series (indicated byfilter andaggregation) and the threshold (indicated bythresholdValue). 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.

OnlyCOMPARISON_LT andCOMPARISON_GT are supported currently.

thresholdValue

number

A value against which to compare the time series.

duration

string (Duration format)

Required. The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in theaggregations 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.

trigger

object (Trigger)

The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified byfilter andaggregations, or by the ratio, ifdenominatorFilter anddenominatorAggregations are specified.

evaluationMissingData

enum (EvaluationMissingData)

A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. To use this control, the value of theduration field must be greater than or equal to 60 seconds.

Aggregation

Describes how to combine multiple time series to provide a different view of the data. Aggregation of time series is done in two steps. First, each time series in the set isaligned to the same time interval boundaries, then the set of time series is optionallyreduced in number.

Alignment consists of applying theperSeriesAligner operation to each time series after its data has been divided into regularalignmentPeriod time intervals. This process takesall 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 acrossSeriesReducer to all the time series, optionally sorting the time series into subsets withgroupByFields, and applying the reducer to each subset.

The raw time series data can contain a huge amount of information from multiple sources. Alignment and reduction transforms this mass of data into a more manageable and representative collection of data, for example "the 95% latency across the average of all tasks in a cluster". This representative data can be more easily graphed and comprehended, and the individual time series data is still available for later drilldown. For more details, seeFiltering and aggregation.

JSON representation
{"alignmentPeriod":string,"perSeriesAligner":enum (Aligner),"crossSeriesReducer":enum (Reducer),"groupByFields":[string]}
Fields
alignmentPeriod

string (Duration format)

ThealignmentPeriod specifies a time interval, in seconds, that is used to divide the data in all thetime 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 thanALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the alignerALIGN_NONE is specified, then this field is ignored.

The maximum value of thealignmentPeriod is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.

perSeriesAligner

enum (Aligner)

AnAligner describes how to bring the data points in a single time series into temporal alignment. Except forALIGN_NONE, all alignments cause all the data points in analignmentPeriod to be mathematically grouped together, resulting in a single data point for eachalignmentPeriod 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 themetricKind andvalueType of the original time series. Alignment can change themetricKind or thevalueType of the time series.

Time series data must be aligned in order to perform cross-time series reduction. IfcrossSeriesReducer is specified, thenperSeriesAligner must be specified and not equal toALIGN_NONE andalignmentPeriod must be specified; otherwise, an error is returned.

crossSeriesReducer

enum (Reducer)

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 themetricKind and thevalueType of the original time series. Reduction can yield a time series with a differentmetricKind orvalueType than the input time series.

Time series data must first be aligned (seeperSeriesAligner) in order to perform cross-time series reduction. IfcrossSeriesReducer is specified, thenperSeriesAligner must be specified, and must not beALIGN_NONE. AnalignmentPeriod must also be specified; otherwise, an error is returned.

groupByFields[]

string

The set of fields to preserve whencrossSeriesReducer is specified. ThegroupByFields 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. ThecrossSeriesReducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly containsresource.type. Fields not specified ingroupByFields are aggregated away. IfgroupByFields 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. IfcrossSeriesReducer is not defined, this field is ignored.

Aligner

TheAligner specifies the operation that will be applied to the data points in each alignment period in a time series. Except forALIGN_NONE, which specifies that no operation be applied, each alignment operation replaces the set of data values in each alignment period with a single value: the result of applying the operation to the data values. An aligned time series has a single data value at the end of eachalignmentPeriod.

An alignment operation can change the data type of the values, too. For example, if you apply a counting operation to boolean values, the datavalueType in the original time series isBOOLEAN, but thevalueType in the aligned result isINT64.

Enums
ALIGN_NONENo alignment. Raw data is returned. Not valid if cross-series reduction is requested. ThevalueType of the result is the same as thevalueType of the input.
ALIGN_DELTA

Align and convert toDELTA. The output isdelta = y1 - y0.

This alignment is valid forCUMULATIVE andDELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. ThevalueType of the aligned result is the same as thevalueType of the input.

ALIGN_RATE

Align and convert to a rate. The result is computed asrate = (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 thealignmentPeriod.

This aligner is valid forCUMULATIVE andDELTA 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 aGAUGE metric withvalueTypeDOUBLE.

If, by "rate", you mean "percentage change", see theALIGN_PERCENT_CHANGE aligner instead.

ALIGN_INTERPOLATEAlign by interpolating between adjacent points around the alignment period boundary. This aligner is valid forGAUGE metrics with numeric values. ThevalueType of the aligned result is the same as thevalueType of the input.
ALIGN_NEXT_OLDERAlign 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 forGAUGE metrics. ThevalueType of the aligned result is the same as thevalueType of the input.
ALIGN_MINAlign the time series by returning the minimum value in each alignment period. This aligner is valid forGAUGE andDELTA metrics with numeric values. ThevalueType of the aligned result is the same as thevalueType of the input.
ALIGN_MAXAlign the time series by returning the maximum value in each alignment period. This aligner is valid forGAUGE andDELTA metrics with numeric values. ThevalueType of the aligned result is the same as thevalueType of the input.
ALIGN_MEANAlign the time series by returning the mean value in each alignment period. This aligner is valid forGAUGE andDELTA metrics with numeric values. ThevalueType of the aligned result isDOUBLE.
ALIGN_COUNTAlign the time series by returning the number of values in each alignment period. This aligner is valid forGAUGE andDELTA metrics with numeric or Boolean values. ThevalueType of the aligned result isINT64.
ALIGN_SUMAlign the time series by returning the sum of the values in each alignment period. This aligner is valid forGAUGE andDELTA metrics with numeric and distribution values. ThevalueType of the aligned result is the same as thevalueType of the input.
ALIGN_STDDEVAlign the time series by returning the standard deviation of the values in each alignment period. This aligner is valid forGAUGE andDELTA metrics with numeric values. ThevalueType of the output isDOUBLE.
ALIGN_COUNT_TRUEAlign the time series by returning the number ofTrue values in each alignment period. This aligner is valid forGAUGE metrics with Boolean values. ThevalueType of the output isINT64.
ALIGN_COUNT_FALSEAlign the time series by returning the number ofFalse values in each alignment period. This aligner is valid forGAUGE metrics with Boolean values. ThevalueType of the output isINT64.
ALIGN_FRACTION_TRUEAlign the time series by returning the ratio of the number ofTrue values to the total number of values in each alignment period. This aligner is valid forGAUGE metrics with Boolean values. The output value is in the range [0.0, 1.0] and hasvalueTypeDOUBLE.
ALIGN_PERCENTILE_99Align the time series by usingpercentile aggregation. The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid forGAUGE andDELTA metrics with distribution values. The output is aGAUGE metric withvalueTypeDOUBLE.
ALIGN_PERCENTILE_95Align the time series by usingpercentile aggregation. The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid forGAUGE andDELTA metrics with distribution values. The output is aGAUGE metric withvalueTypeDOUBLE.
ALIGN_PERCENTILE_50Align the time series by usingpercentile aggregation. The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid forGAUGE andDELTA metrics with distribution values. The output is aGAUGE metric withvalueTypeDOUBLE.
ALIGN_PERCENTILE_05Align the time series by usingpercentile aggregation. The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid forGAUGE andDELTA metrics with distribution values. The output is aGAUGE metric withvalueTypeDOUBLE.
ALIGN_PERCENT_CHANGE

Align and convert to a percentage change. This aligner is valid forGAUGE andDELTA metrics with numeric values. This alignment returns((current - previous)/previous) * 100, where the value ofprevious is determined based on thealignmentPeriod.

If the values ofcurrent andprevious are both 0, then the returned value is 0. If onlyprevious 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. WhileDELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is aGAUGE metric withvalueTypeDOUBLE.

Reducer

A Reducer operation describes how to aggregate data points from multiple 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.

Enums
REDUCE_NONENo cross-time series reduction. The output of theAligner is returned.
REDUCE_MEANReduce by computing the mean value across time series for each alignment period. This reducer is valid forDELTA andGAUGE metrics with numeric or distribution values. ThevalueType of the output isDOUBLE.
REDUCE_MINReduce by computing the minimum value across time series for each alignment period. This reducer is valid forDELTA andGAUGE metrics with numeric values. ThevalueType of the output is the same as thevalueType of the input.
REDUCE_MAXReduce by computing the maximum value across time series for each alignment period. This reducer is valid forDELTA andGAUGE metrics with numeric values. ThevalueType of the output is the same as thevalueType of the input.
REDUCE_SUMReduce by computing the sum across time series for each alignment period. This reducer is valid forDELTA andGAUGE metrics with numeric and distribution values. ThevalueType of the output is the same as thevalueType of the input.
REDUCE_STDDEVReduce by computing the standard deviation across time series for each alignment period. This reducer is valid forDELTA andGAUGE metrics with numeric or distribution values. ThevalueType of the output isDOUBLE.
REDUCE_COUNTReduce by computing the number of data points across time series for each alignment period. This reducer is valid forDELTA andGAUGE metrics of numeric, Boolean, distribution, and stringvalueType. ThevalueType of the output isINT64.
REDUCE_COUNT_TRUEReduce by computing the number ofTrue-valued data points across time series for each alignment period. This reducer is valid forDELTA andGAUGE metrics of BooleanvalueType. ThevalueType of the output isINT64.
REDUCE_COUNT_FALSEReduce by computing the number ofFalse-valued data points across time series for each alignment period. This reducer is valid forDELTA andGAUGE metrics of BooleanvalueType. ThevalueType of the output isINT64.
REDUCE_FRACTION_TRUEReduce by computing the ratio of the number ofTrue-valued data points to the total number of data points for each alignment period. This reducer is valid forDELTA andGAUGE metrics of BooleanvalueType. The output value is in the range [0.0, 1.0] and hasvalueTypeDOUBLE.
REDUCE_PERCENTILE_99Reduce by computing the99th percentile of data points across time series for each alignment period. This reducer is valid forGAUGE andDELTA metrics of numeric and distribution type. The value of the output isDOUBLE.
REDUCE_PERCENTILE_95Reduce by computing the95th percentile of data points across time series for each alignment period. This reducer is valid forGAUGE andDELTA metrics of numeric and distribution type. The value of the output isDOUBLE.
REDUCE_PERCENTILE_50Reduce by computing the50th percentile of data points across time series for each alignment period. This reducer is valid forGAUGE andDELTA metrics of numeric and distribution type. The value of the output isDOUBLE.
REDUCE_PERCENTILE_05Reduce by computing the5th percentile of data points across time series for each alignment period. This reducer is valid forGAUGE andDELTA metrics of numeric and distribution type. The value of the output isDOUBLE.

ForecastOptions

Options used when forecasting the time series and testing the predicted value against the threshold.

JSON representation
{"forecastHorizon":string}
Fields
forecastHorizon

string (Duration format)

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 configuredduration, then the time series is considered to be failing. The forecast horizon can range from 1 hour to 60 hours.

ComparisonType

Specifies an ordering relationship on two arguments, calledleft andright.

Enums
COMPARISON_UNSPECIFIEDNo ordering relationship is specified.
COMPARISON_GTTrue if the left argument is greater than the right argument.
COMPARISON_GETrue if the left argument is greater than or equal to the right argument.
COMPARISON_LTTrue if the left argument is less than the right argument.
COMPARISON_LETrue if the left argument is less than or equal to the right argument.
COMPARISON_EQTrue if the left argument is equal to the right argument.
COMPARISON_NETrue if the left argument is not equal to the right argument.

Trigger

Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a{count: 1} trigger is used.

JSON representation
{// Union fieldtype can be only one of the following:"count":integer,"percent":number// End of list of possible types for union fieldtype.}
Fields
Union fieldtype. A type of trigger.type can be only one of the following:
count

integer

The absolute number of time series that must fail the predicate for the condition to be triggered.

percent

number

The percentage of time series that must fail the predicate for the condition to be triggered.

EvaluationMissingData

A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. This control doesn't affect metric-absence policies.

Enums
EVALUATION_MISSING_DATA_UNSPECIFIEDAn unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP.
EVALUATION_MISSING_DATA_INACTIVEIf there is no data to evaluate the condition, then evaluate the condition as false.
EVALUATION_MISSING_DATA_ACTIVEIf there is no data to evaluate the condition, then evaluate the condition as true.
EVALUATION_MISSING_DATA_NO_OPDo not evaluate the condition to any value if there is no data.

MetricAbsence

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 specifiedduration.

JSON representation
{"filter":string,"aggregations":[{object (Aggregation)}],"duration":string,"trigger":{object (Trigger)}}
Fields
filter

string

Required. Afilter that identifies which time series should be compared with the threshold.

The filter is similar to the one that is specified in thetimeSeries.list request (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.

aggregations[]

object (Aggregation)

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 thetimeSeries.list request. It is advisable to use thetimeSeries.list method when debugging this field.

duration

string (Duration format)

Required. The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned.

trigger

object (Trigger)

The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified byfilter andaggregations.

LogMatch

A condition type that checks whether a log message in thescoping project satisfies the given filter. Logs from other projects in the metrics scope are not evaluated.

JSON representation
{"filter":string,"labelExtractors":{string:string,...}}
Fields
filter

string

Required. A logs-based filter. SeeAdvanced Logs Queries for how this filter should be constructed.

labelExtractors

map (key: string, value: string)

Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that matchfilter. Each combination of extracted values is treated as a separate rule for the purposes of triggering notifications. Label keys and corresponding values can be used in notifications generated by this condition.

Please seethe documentation on logs-based metricvalueExtractors for syntax and examples.

MonitoringQueryLanguageCondition

A condition type that allows alerting policies to be defined usingMonitoring Query Language.

JSON representation
{"query":string,"duration":string,"trigger":{object (Trigger)},"evaluationMissingData":enum (EvaluationMissingData)}
Fields
query

string

Monitoring Query Language query that outputs a boolean stream.

duration

string (Duration format)

Optional. The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in theaggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly. The default value is zero.

trigger

object (Trigger)

The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified byfilter andaggregations, or by the ratio, ifdenominatorFilter anddenominatorAggregations are specified.

evaluationMissingData

enum (EvaluationMissingData)

A condition control that determines how metric-threshold conditions are evaluated when data stops arriving.

PrometheusQueryLanguageCondition

A condition type that allows alerting policies to be defined usingPrometheus Query Language (PromQL).

The PrometheusQueryLanguageCondition message contains information from a Prometheus alerting rule and its associated rule group.

A Prometheus alerting rule is describedhere. The semantics of a Prometheus alerting rule is describedhere.

A Prometheus rule group is describedhere. The semantics of a Prometheus rule group is describedhere.

Because Cloud Alerting has no representation of a Prometheus rule group resource, we must embed the information of the parent rule group inside each of the conditions that refer to it. We must also update the contents of all Prometheus alerts in case the information of their rule group changes.

The PrometheusQueryLanguageCondition protocol buffer combines the information of the corresponding rule group and alerting rule. The structure of the PrometheusQueryLanguageCondition protocol buffer does NOT mimic the structure of the Prometheus rule group and alerting rule YAML declarations. The PrometheusQueryLanguageCondition protocol buffer may change in the future to support future rule group and/or alerting rule features. There are no new such features at the present time (2023-06-26).

JSON representation
{"query":string,"duration":string,"evaluationInterval":string,"labels":{string:string,...},"ruleGroup":string,"alertRule":string,"disableMetricValidation":boolean}
Fields
query

string

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.

duration

string (Duration format)

Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero.

evaluationInterval

string (Duration format)

Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group.

labels

map (key: string, value: string)

Optional. Labels to add to or overwrite in the PromQL query result. Label namesmust be valid. Label values can betemplatized by using variables. The only available variable names are the names of the labels in the PromQL result, including "__name__" and "value". "labels" may be empty.

ruleGroup

string

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.

alertRule

string

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 avalid Prometheus label name. This field may not exceed 2048 Unicode characters in length.

disableMetricValidation

boolean

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 themonitoring.alertPolicyViewer role are able to see the name of the non-existent metric in the alerting policy condition.

SqlCondition

A condition that allows alerting policies to be defined using GoogleSQL. SQL conditions examine a sliding window of logs using GoogleSQL. Alert policies with SQL conditions may incur additional billing.

JSON representation
{"query":string,// Union fieldschedule can be only one of the following:"minutes":{object (Minutes)},"hourly":{object (Hourly)},"daily":{object (Daily)}// End of list of possible types for union fieldschedule.// Union fieldevaluate can be only one of the following:"rowCountTest":{object (RowCountTest)},"booleanTest":{object (BooleanTest)}// End of list of possible types for union fieldevaluate.}
Fields
query

string

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, logName, severity, httpRequest, resource, labelsFROM  my-project.global._Default._AllLogsWHERE  httpRequest IS NOT NULL
Union fieldschedule. The schedule indicates how often the query should be run.schedule can be only one of the following:
minutes

object (Minutes)

Schedule the query to execute every so many minutes.

hourly

object (Hourly)

Schedule the query to execute every so many hours.

daily

object (Daily)

Schedule the query to execute every so many days.

Union fieldevaluate. The test to be run against the SQL result set.evaluate can be only one of the following:
rowCountTest

object (RowCountTest)

Test the row count against a threshold.

booleanTest

object (BooleanTest)

Test the boolean value in the indicated column.

Minutes

Used to schedule the query to run every so many minutes.

JSON representation
{"periodicity":integer}
Fields
periodicity

integer

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.

Hourly

Used to schedule the query to run every so many hours.

JSON representation
{"periodicity":integer,"minuteOffset":integer}
Fields
periodicity

integer

Required. The number of hours between runs. Must be greater than or equal to 1 hour and less than or equal to 48 hours.

minuteOffset

integer

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.

Daily

Used to schedule the query to run every so many days.

JSON representation
{"periodicity":integer,"executionTime":{object (TimeOfDay)}}
Fields
periodicity

integer

Required. The number of days between runs. Must be greater than or equal to 1 day and less than or equal to 31 days.

executionTime

object (TimeOfDay)

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.

TimeOfDay

Represents a time of day. The date and time zone are either not significant or are specified elsewhere. An API may choose to allow leap seconds. Related types aregoogle.type.Date andgoogle.protobuf.Timestamp.

JSON representation
{"hours":integer,"minutes":integer,"seconds":integer,"nanos":integer}
Fields
hours

integer

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

integer

Minutes of an hour. Must be greater than or equal to 0 and less than or equal to 59.

seconds

integer

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.

nanos

integer

Fractions of seconds, in nanoseconds. Must be greater than or equal to 0 and less than or equal to 999,999,999.

RowCountTest

A test that checks if the number of rows in the result set violates some threshold.

JSON representation
{"comparison":enum (ComparisonType),"threshold":string}
Fields
comparison

enum (ComparisonType)

Required. The comparison to apply between the number of rows returned by the query and the threshold.

threshold

string (int64 format)

Required. The value against which to compare the row count.

BooleanTest

A test that uses an alerting result in a boolean column produced by the SQL query.

JSON representation
{"column":string}
Fields
column

string

Required. The name of the column containing the boolean value. If the value in a row is NULL, that row is ignored.

ConditionCombinerType

Operators for combining conditions.

Enums
COMBINE_UNSPECIFIEDAn unspecified combiner.
ANDCombine conditions using the logicalAND 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.
ORCombine conditions using the logicalOR operator. An incident is created if any of the listed conditions is met.
AND_WITH_MATCHING_RESOURCECombine conditions using logicalAND operator, but unlike the regularAND option, an incident is created only if all conditions are met simultaneously on at least one resource.

AlertStrategy

Control over how the notification channels innotificationChannels are notified when this alert fires.

JSON representation
{"notificationRateLimit":{object (NotificationRateLimit)},"notificationPrompts":[enum (NotificationPrompt)],"autoClose":string,"notificationChannelStrategy":[{object (NotificationChannelStrategy)}]}
Fields
notificationRateLimit

object (NotificationRateLimit)

Required for log-based alerting policies, i.e. policies with aLogMatch condition.

This limit is not implemented for alerting policies that do not have a LogMatch condition.

notificationPrompts[]

enum (NotificationPrompt)

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].

autoClose

string (Duration format)

If an alerting policy that was active has no data for this long, any open incidents will close

notificationChannelStrategy[]

object (NotificationChannelStrategy)

Control how notifications will be sent out, on a per-channel basis.

NotificationRateLimit

Control over the rate of notifications sent to this alerting policy's notification channels.

JSON representation
{"period":string}
Fields
period

string (Duration format)

Not more than one notification perperiod.

NotificationPrompt

Control when notifications will be sent out.

Enums
NOTIFICATION_PROMPT_UNSPECIFIEDNo strategy specified. Treated as error.
OPENEDNotify when an incident is opened.
CLOSEDNotify when an incident is closed.

NotificationChannelStrategy

Control over how the notification channels innotificationChannels are notified when this alert fires, on a per-channel basis.

JSON representation
{"notificationChannelNames":[string],"renotifyInterval":string}
Fields
notificationChannelNames[]

string

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 notificationChannels field of this AlertPolicy. The format is:

projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]
renotifyInterval

string (Duration format)

The frequency at which to send reminder notifications for open incidents.

Methods

create

Creates a new alerting policy.

delete

Deletes an alerting policy.

get

Gets a single alerting policy.

list

Lists the existing alerting policies for the workspace.

patch

Updates an alerting policy.

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Last updated 2025-10-23 UTC.